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Asymmetric impacts of public and private investments on the non-oil GDP of Saudi Arabia
Accepted Manuscript Asymmetric impacts of public and private investments on the non-oil GDP of Saudi Arabia Walid Mensi, Syed Jawad Hussain Shahzad, Shawkat Hammoudeh, Khamis Hamed Al-Yahyaee PII:
S2110-7017(17)30090-2
DOI:
10.1016/j.inteco.2017.10.003
Reference:
INTECO 141
To appear in:
International Economics
Received Date: 24 April 2017 Revised Date:
19 October 2017
Accepted Date: 19 October 2017
Please cite this article as: Mensi, W., Hussain Shahzad, S.J., Hammoudeh, S., Al-Yahyaee, K.H., Asymmetric impacts of public and private investments on the non-oil GDP of Saudi Arabia, International Economics (2017), doi: 10.1016/j.inteco.2017.10.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Asymmetric impacts of public and private investments on the non-oil GDP of Saudi Arabia Walid Mensia,b , Syed Jawad Hussain Shahzadc, Shawkat Hammoudeh c,d, Khamis Hamed AlYahyaeeb a
Lebow College of Business, Drexel University, Philadelphia, United States Email: [email protected]
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d
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Energy and Sustainable Development (ESD), Montpellier Business School, Montpellier, France Email: [email protected]
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Abstract This paper investigates the impact of four major macroeconomic variables (private investment, public investment, oil production and inflation) on non-oil GDP in the oil-based Saudi Arabia. To this end, we use the nonlinear ARDL (NARDL) and the causality-in-quantiles methods to measure the impact of these variables on non-oil GDP. The results show that past non-oil GDP shocks affect current non-oil GDP strongly in short term. Moreover, a surge in public investment increases non-oil GDP in both the short- and long-run, while a negative private investment shock reduces non-oil GDP in both the short- and long-run. Furthermore, positive (negative) oil production shocks increase the nonoil GDP also in the short- and long-run. In addition, we find a positive relationship between negative and positive inflation shocks and non-oil GDP in the long run, while negative inflation shocks decrease non-oil GDP. Using the nonparametric causality-in- quantile approach, we find that causality-in-the mean and causality-in-the variance emanating from the four explanatory variables vary across the quantiles. Finally, non-oil GDP does not Granger cause these macroeconomic variables. Those non-standard macroeconomic results for this major oil exporter are different from those for well-diversified developed countries. Regardless, they have important implications for Saudi policy makers involved in the Vision 2030 initiative, international organizations and institutional investors.
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c
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Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman Email: [email protected] Email: [email protected]
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b
Department of Finance and Accounting, University of Tunis El Manar, Tunis, Tunisia
JEL classification: G14; G15 Keywords: Saudi Arabia; Investments; Macroeconomic variables; non-oil GDP; NARD; Causality-in-quantiles.
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ACCEPTED MANUSCRIPT 1. Introduction Investment and economic growth are two important macroeconomic variables that are strongly related in all economies. They are interconnected since investment (public or private) is a major pillar for growth in any economy. Investment refers to any economic activity that
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involves providing and combining resources to produce goods and services, leading to more output and greater economic growth. Putting crowding out aside, private and public investments are likely complementary economic activities that harness greater resources for
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producing more output and increasing gross domestic product (GDP). Dreger and Reimer (2016) provide evidence that shows that a lack of public investment may restrict private
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investment and arrest GDP growth in the euro area. Public investment through active fiscal policy raises output in both the short- and long-term and in most cases “crowds in” private investment in advanced economies (Abiad et al., 2015).
Investment decisions are widely related to major economic and political events or
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phases of the business cycle, as well as to the volatility of commodity markets like the crude oil market for both producing and consuming countries. The recent global financial crisis (GFC) of 2008-2009 and the European sovereign-debt crisis (ESDC) of 2010-2012 have put
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financial constraints on governments and affected investment decisions and economic growth (Barbosa et al., 2016). Among the countries that are affected by these events are the oil-rich
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member countries of the Gulf Cooperation Council (GCC) which had experienced a drastic drop in oil revenues as a result of the recent collapse of oil prices, following the historic increase in summer 2008. The oil price is a key propeller of investment and economic growth for Saudi Arabia, the world’s largest oil exporter. In fact, the Kingdom is the largest economy in the GCC, Middle East and North Africa (MENA) regions, the second largest global oil producer and its oil and non-oil GDP depends mainly on public investment which fuels economic growth. Further, the Saudi government’s revenues are significantly based on the
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ACCEPTED MANUSCRIPT proceeds from oil exports. The Saudi government’s spending is equally dependent on oil revenues.1 More importantly, oil prices dropped in June 2014, leading to lower investment and slower economic growth in the country. On the other hand, inflation in the GCC countries has moved in concordance with
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public investment and oil prices in those years.2 The inflation in Saudi Arabian, in particular, is determined through the cost of living Index (CLI) and the wholesale price index (PCI).3 The Saudi economy is based on public investment and public/private consumption
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which are the major drivers of its total GDP. The Saudi public investment is based particularly on the oil sector activity. As we indicated above, after a surge in prosperity over
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the last years which was fueled by rising oil prices, the Saudi economy is at an inflection point. In fact, the recent plunges in oil prices have pushed the Saudi government to introduce substantial reforms in order to wane its economy off oil revenues, strengthen the non-oil GDP and foster the private sector which is dominated by a handful of big businesses in the services
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subsectors. 4 One of the main objectives of the Saudi government in its Vision 2030 is to develop a well-diversified economy by 2030.
This paper purposes to examine the impact of private and public investments, oil
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production and inflation on non-oil GDP in Saudi Arabia. We focus on the Saudi non-oil GDP
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because of the recently developed Vision 2030 of the Kingdom which aims to diversify its According the BP Statistical Review of World Energy (June 2015), the total oil reserves of Saudi Arabia were 267.0 billion barrels at the end of 2014 and its total oil production reached 11.701 million barrels/day in that period. Its real GDP had risen from Saudi Riyal (SAR) 863.5 billion at the end of Q1:1990 to SAR 2,464.3 billion at the end of Q4:2014, a 284.5% increase. Its real fixed investment was SAR 182.7 billion at the end of 1990 and is expected to reach SAR 731.4 billion near the end of 2018. The government (private) consumption increased from SAR 188.7 (SAR 290.1) billion in 1990Q1 to SAR 582.6 (802.4) billion at the end of 2014 and is expected to reach SAR 637.1 (933.3) billion at the end of 2018.1 Note that a large part of the total GDP (i.e., more than 60%) of Saudi Arabia is drawn from the public sector. These statistics are sourced from the IHS Global Insight. 2 Based on the World Development Indicators statistics, CPI changed between 1990 and 2014 from 0.929 to 2.651 for Bahrain, 9.833 to 2.908 for Kuwait, 3.000 to 3.082 for Qatar and 2.0771 to 2.6700 for Saudi Arabia. More recently, inflation rates vary among the GCC member countries, ranging between 0.4% for Oman and 3.67 % for UAE by end of January 2015 over the previous year. For further information on GCC inflation, the reader can visit http://gccstat.org/en/about/news/january-inflation-2015. 3 https://www.samba.com/.../Inflation_in_Saudi_Arabia_Eng.pdf. 4 For further information on Vision 2030, the reader may use the following link: vision2030.gov.sa/download/file/fid/417.
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ACCEPTED MANUSCRIPT economy, encourage private investments and stimulate non-oil GDP. Th objective of this vision plan is to diversify revenues and avoid depending on oil proceeds as the major economic pillar of the Kingdom. In fact, there is a general trend among the GCC oilexporting countries to focus more on non-oil GDP, and Saudi Arabia is the leader in this
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regard. The choice of the considered exogenous variables in the current study can be explained by their strong linkages to the Saudi economy as discussed above.5 As indicated earlier, the private and public investments can be complementary in an oil-exporting,
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developing economy such as that of Saudi Arabia where the latter “crowds in” the former, which is different of what we may observe in well diversified developed countries most of the
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time. We have also selected the inflation variable because mild and stable inflation makes it easier for businesses in the country to make better investment decisions and for wages to rise steadily and stimulate demand (Pradhan et al., 2015).
For this end, we use the nonlinear autoregressive distributed lag (NARDL) model and
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the nonparametric causality-in-quantile approach. These methods are robust and suitable to nonlinearity that is usually dominant in the data, which is caused by asymmetry and the presence of structural breaks in the economic time series. The NARDL model is generally an
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asymmetric extension of the linear ARDL approach, which is designed primarily to model the long-run relationships among the variables of interest. In our case, we have chosen the
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NARDL model because our time series most likely have ‘hidden cointegration’ and the positive and negative components of the series are cointegrated due to their connections to oil prices which have experienced major structural breaks (Granger and Yoon, 2002; Raza et al., 2016). Shin et al. (2014) derived the dynamic error-correction representation related to the asymmetric long-run cointegration regression, resulting in the NARDL model. Moreover, 5
More interestingly, these variables are widely studied in the previous literature (see the next section) but are not used to investigate their impact on the non-oil GDP.
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ACCEPTED MANUSCRIPT these authors proposed asymmetric cumulative dynamic multipliers that allow one to trace the asymmetric adjustment patterns, following positive and negative shocks to the exogenous variables. We should note that this paper is the first to examine the validity of the NARDL model through the application of the asymmetric generalized impulse response (AGIR)
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function and asymmetric variance decomposition (AVD) approaches. In fact, we argue that this asymmetric framework may be used as a robustness test of the asymmetric dynamic estimations.
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As for the nonparametric causality-in-quantile method, this approach allows one to investigate the causality between the variables over time and across quantiles. Moreover, it
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allows one to test the nonlinear causality of the k-th order across all quantiles of the distribution of the non-oil GDP-macroeconomic variable movements in the current study. As indicated, this study tests not only the non-oil GDP-macroeconomic variable causality in the first order moments (or returns) but also in the second order moments (or variance). The
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nonparametric causality-in-quantile method used in this study is robust to misspecification errors, structural breaks and frequent outliers, which are commonly found in economic time series (Balcilar et al., 2016).
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This paper differs from and adds to the related literature on the investment-growth nexus in five balancing ways. First, we investigate the short-run and long-run relationships
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between non-oil GDP and major related macroeconomic variables including private investments, public investments, oil production and inflation in the largest global oil exporter. Knowing the relevant time horizon is extremely important for decision makers (e.g., speculators, institutional investors, policy makers including annual government budget makers and longer term development planners, etc.), particularly when it comes to choosing the types of government investments and the overall asset allocation for a particular portfolio. Government policy makers embark on annual budgets to deal with short run objectives and
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ACCEPTED MANUSCRIPT multiple year development plans to achieve longer term goals. On the other hand, investors may have multiple investment types in place to account for different financial goals that result in various time horizons. Many macroeconomic variables are economic activity variables, and thus they co-
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move with the business cycle. For Saudi Arabia, the business cycle has two major drivers: oil revenues and government’s stabilization policy and those drivers are related. They in turn move all macroeconomic variables. However, economic policy and actions of decision
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makers affect them differently over time. Moreover, due to heterogeneity of preferences, economic agents may not behave similarly over the different phases of the business cycle
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which can cause asymmetry. Fiscal policy, particularly government spending in Saudi Arabia, will be more responsive to recessions than expansions because of the fear of public dissatisfaction with government policies. The Saudi central bank (SAMA) will be much more willing to change interest rates and regulations during difficult times than during the long run.
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In the short run, there are over- and under- shootings in macroeconomic variables but in the long run the variables converge to the long-run equilibrium, which yields different behaviors and trajectories between the short and long run.
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Interestingly, we choose to focus on a frontier country because Saudi Arabia is a heavily oil dependent economy which has recently acknowledged the potential of the non-oil
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part of its economy and that there is a real danger to the Saudi economy in the future if the world’s demand for oil turns the other corner. Also, the Kingdom is the largest GCC and Middle Eastern economy and accounts for around 47% of the region’s GDP.6 Second, we use the nonlinear ARDL because the relationship between the non-oil GDP and the macroeconomic variables can differ in the short- and long-run as oil prices follow short- and long-term events and stages of the business cycle.
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http://www.propelconsult.com/non-oil-sector-drives-gcc-gdp-growth/.
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ACCEPTED MANUSCRIPT Third, we check the validity of the NARDL model through the use of the asymmetric generalized impulse response function and the asymmetric variance decomposition methods. Fourth, we use the nonparametric causality-in-quantile method to examine the relationship between the variables under different economic conditions, which allows the parameter to
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vary across the quantiles. The linear causality detection techniques heavily rely on conditional means, and therefore fail to capture the conditional tail distribution of the time series. The nonparametric causality-in-quantile approach is robust to misspecification errors, structural
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breaks and frequent outliers, which are commonly found in economic time series (Balcilar et al., 2017). Finally, we complete our study by doing a multiplier analysis which permits us to
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determine the contributions of the private and public investments to non-oil GDP in Saudi Arabia.
Our empirical results show strong evidence of a nonlinear relationship between the macroeconomic variables under consideration (i.e., public investments, private investments,
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CPI and oil production) and the non-oil GDP for Saudi Arabia. The NARDL model’s results also reveal that past non-oil GDP shocks affect current non-oil GDP in the short run. Additionally, previous positive public investment shocks increase the non-oil GDP in the
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short-run. However, a negative private investment shock decreases non-oil GDP in the shortrun. More interestingly, negative CPI shocks and negative oil production shocks both
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decrease the non-oil GDP in the short-run, probably due to heightened uncertainty about the sustainability of oil exports and sprouting inflation. On the other hand, positive oil production shocks increase the non-oil GDP in the short term. In the long run, a positive public investment shock significantly increases non-oil GDP as it involves increase in governmental capital expenditures, whereas a negative public investment shock does not impact non-oil GDP probably due to a rising compensating social fiscal spending during difficult times. We find a negative linkage between private investment
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ACCEPTED MANUSCRIPT and the non-oil GDP in the long-run probably due to the countries’ heavy dependence on oil revenues which make up 80% of the government budget and probably due to the occurrence of some crowding out of public investment. Both negative and positive CPI shocks have a significant positive impact on non-oil GDP, which may have to do with rising aggregate
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demand in an environment of falling and rising prices in the country. Further, a positive shock in oil production significantly increases the non-oil GDP as a good part of this sector is oilrelated, while a negative oil production shock reduces it.
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To deepen the analysis, we use the nonparametric causality-in-quantiles and find that previous public investment Granger causes in-mean and in-variance the non-oil GDP across
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various quantiles, with the exception of the highest and lowest quantiles. In contrast, we find little evidence that private investment Granger causes the non-oil GDP probably due to its subordinate role to public investment. Oil production Granger causes in-the mean and in-the variance the non-oil GDP for almost all quantiles (under different economic conditions). For
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CPI, we find no Granger causality-in-mean and in-variance of the non-oil GDP. In contrast, the non-oil GDP does no Granger-cause the macroeconomic variables, probably due to the dominance of the oil sector in this highly oil-dependent economy.
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The remainder of this study is organized as follows. Section 2 provides a review of the literature. Section 3 discusses the methodology. Section 4 describes the data and presents
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some preliminary analyses. Section 5 discusses the empirical results and draws implications for risk management. Section 6 provides concluding remarks.
2. Literature review In the related literature, many studies taking different strands have examined the linkages between investments and economic growth. The first strand deals with the impact of foreign direct investment (FDI) inflows and portfolio investment on long-run economic growth. The
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ACCEPTED MANUSCRIPT second examines the impact of public investment on long-run economic growth, while the third strand focuses on the relationship between private investment and economic growth. The first strand examines the impact of FDI on economic growth. Feeny et al. (2014) examine the impact of FDI on the economies of the Pacific region and document that this
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impact is positive but lower in Pacific countries than in host countries on average. The results show that a 10% increase in the ratio of FDI to host gross domestic product (GDP) is associated with an economic growth of about 2% in all countries on average. The impact in
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Pacific countries falls to between 0.1 and 0.4%. Feeny et al. (2014) suggest that FDI displaces domestic investment in the region.
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Albulescu (2015) explores the impact of FDI and foreign portfolio investment inflows on long-term economic growth in Central and Eastern European countries. The results demonstrate that both direct and portfolio investments exert a positive influence on the longterm economic growth, when we also consider equity and investment funds’ instruments. The
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author shows that incentive packages should be oriented toward both types of investments. Adam (2009) analyzes the impact of FDI and domestic investment on economic growth in Sub-Saharan Africa for the period 1990–2003. The results show a strong positive nexus
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between domestic investment and economic growth The study also finds that FDI has an initial negative effect on domestic investment and a subsequent positive effect in later periods
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for the panel of the studied countries. The sign and magnitude of the current and lagged FDI coefficients suggest a net crowding out effect. Gui-Diby (2014) examines the impact of FDI inflows on economic growth in 50
African countries over the period 1980–2009 and shows that these inflows have a significant positive impact on economic growth and that the scarcity of qualified human resources does not limit the impact of FDI. More recently, Iamsiraroj (2016) studies the relationship between FDI and economic growth using the simultaneous system of equations approach for 124
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In the second strand of the literature, Kireyev (1998) explores the linkages between public spending and non-oil GDP growth. This author finds a significant and positive relationship between public spending and economic growth in non-oil GDP (i.e., a 1%
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increase in public expenditure causes about a half percent increase in non-oil GDP). Buffie (1995) investigates the short- and long-run effects of cutting investment in social
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infrastructure in a simple perfect foresight model. The author shows that private investment increases in the short run, provided that the intertemporal elasticity of substitution is not extremely large. Furthermore, the author finds that inflation may be higher throughout the adjustment process if a modest degree of intertemporal substitution is possible.
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Jun (2003) assesses China’s economic growth with respect to the growth of investment at the aggregate level and explores the investment–growth nexus during the period of high growth (i.e., 1978-2000). The author demonstrates that China has since realized its high
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growth, without giving rise to an increasing proportion of investment to GDP and to a rise in the incremental capital-output ratios (ICORs). Moreover, investment efficiency was largely
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reaped through the rural industrialization and proliferation of small firms in the non-state sector.
Qin et al. (2006) examine investment-driven growth in China and show an existence of
a long-run positive relationship between investment and economic growth, but the causality runs from the latter to the former. The authors nullify the view that investment drives output growth. Ghazi and Starr (2011) investigate the relationship between government spending and non-oil GDP in Saudi Arabia. Those authors find that increases in government spending have
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ACCEPTED MANUSCRIPT a positive and significant long-run effect on the rate of economic growth. In addition, they examine the effects of current expenditure on economic growth and demonstrate that government investment in infrastructure and productive capacity has been less growthenhancing in Saudi Arabia than programs designed to improve the administration and
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operation of government entities. Alshahrani and Alsadiq (2014) investigate the impacts of different types of government expenditures on economic growth in Saudi Arabia, using different methods to investigate the short- and long-run effects of these expenditures on
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economic growth. The results indicate that while private domestic and public investments, as well as healthcare expenditure, stimulate growth in the long run, openness to trade and
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spending in the housing sector can also boost production in the short-run.
Mann and Sephton (2015) examine the effects of private investment and different categories of public expenditure (defense, education, health care and housing) on real non-oil GDP in Saudi Arabia, using unit root stationarity and cointegration analysis with vector-error
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correction models. The authors show that public expenditures on health care and defense have decreased real non-oil GDP, while public expenditure on education and housing have very little impact. Furthermore, public expenditures on health crowd-out private investment. Harb
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(2009) investigates the long run and short-run relationships between oil exports, non-oil GDP and investment in five GCC countries. The author considers the effect of cross-sectional
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correlations and applies the corresponding panel unit root tests to study the long-run characteristics of the data series. The author finds that resource exports have no long-run relationship with the macroeconomic variables. In the short-run dynamics, the VAR analysis shows that the effect of oil exports on those variables depends on local policies. Using the general equilibrium approach to fiscal policy, Nakibullah and Islam (2007) explore the effects of government spending on non-oil GDP of Bahrain. The authors find that the positive multiplier effect of permanent domestic government consumption is substantially
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ACCEPTED MANUSCRIPT neutralized by the negative impact of temporary US government spending on non-oil GDP of Bahrain. Illegbinosa et al. (2012) examine the impact of macroeconomic variables (i.e., exchange rate, interest rate, government capital expenditures and government recurrent expenditures) on the performance of the Nigerian economy, using the ordinary least square
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(OLS) and cointegration test analysis. The results show that exchange rate, government capital expenditures and government recurrent expenditures are positively related to non-oil exports, the agricultural sector, the manufacturing sub-sector and gross domestic product,
manufacturing sub-sector and gross domestic product.
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while the interest rate is negatively related to non-oil exports, the agricultural sector, the
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Overall, it is apparent from this review of the literature that previous empirical studies that investigated the linkages across investments and economic growth have arrived at mixed conclusions, by using different empirical methods and datasets. Obviously, there is still more room for fresh research, particularly on Saudi Arabia, that uses more modern techniques and
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makes new contributions. This paper attempts to investigate the impact of public and private investments on non-oil GDP, while taking into account the presence of influential macroeconomic and oil variables in this country, using models suitable for nonlinearity-based
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stylized facts such as structural breaks and the presence of outliers.
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3. Empirical methods
This section presents the nonlinear ARDL model and the nonparametric causality-in-quantile model.
3.1. The nonlinear ARDL model The asymmetric bound-testing approach (NARDL hereafter) developed by Shin et al. (2014) is used in the current study to examine the presence of asymmetric effects in the short- and
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ACCEPTED MANUSCRIPT long-run relationships among non-oil GDP and major macroeconomic variables in Saudi Arabia. The NARDL approach is an asymmetric extension of the linear autoregressive distributed lag (ARDL) procedure proposed by Pesaran et al. (2001). Notably, the dynamic error-correction representation of the NARDL approach allows one to capture asymmetry in
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both the short- and long-run. The model also performs better in small samples, compared to other conventional available counterparts (Romilly et al., 2001). The NARDL is also flexible as it provides valid results regardless of the integration order of the variables, i.e., I(0), I(1) or
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a combination of both (Nusair, 2016).
Following Shin et al. (2014), the NARDL model is built around the following
= + +
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asymmetric long-run equilibrium relationship: 7
(1)
where is a stationary zero-mean error process that represents deviations from the long-run equilibrium, and are the associated asymmetric long-run parameters and xt is the vector
= + +
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of regressors decomposed as:
(2)
where is an arbitrary initial value and and denote the partial sum processes which
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accumulate positive and negative changes in , respectively, and are defined as follows: (3)
= ∑ ∆ = ∑ min (∆ , 0)
(4)
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= ∑ ∆ = ∑ max (∆ , 0)
The following asymmetric error correction model can be obtained:
$
∆ = + + + + ∆ + ! ∆ + ! ∆ " + #
(5)
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See Shin et al. (2014) for a more detailed derivation of the NARDL model.
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ACCEPTED MANUSCRIPT where all variables are as defined above, = − and = − , and the short-run adjustments to positive and negative changes in the explanatory variables xt are captured by ! and ! , respectively. The empirical implementation of the NARDL method entails the same steps as in the
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linear ARDL model. In the first step, the error-correction model in Eq. (5) is estimated by the standard OLS. The second step involves testing for the presence of an asymmetric long-run relationship among the levels of the variables using the bounds testing approach. This can be
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done using either of the following two statistics (Shin et al., 2014). The first is the F-statistic, introduced by Pesaran et al. (2001) and denoted by &'(( , which tests the null hypothesis of no
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cointegration ( = = = 0) against the alternative of cointegration ( ≠ ≠ ≠ 0). The second one is the t-statistic, proposed by Banerjee et al. (1998) and denoted by *+,- , which is suitable for testing the null hypothesis of no cointegration against the alternative of cointegration. Since these two statistics have non-standard distributions that depend on the
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order of integration of the underlying variables, the pragmatic bounds-testing procedure advanced by Pesaran et al. (2001) is also utilized in the NARDL approach. The third step consists of testing for long-run symmetry ( = ) and short-run
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$ 8 symmetry (∑$ / !.,/ = ∑/ !.,/ ) by means of the standard Wald tests. The preferred model
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is selected by starting with p=4 and q=4 and then dropping all insignificant regressors. The results of bound testing and the Wald tests testing the short- and long-run
asymmetries are reported in Table 4. The results of &'(( and *+,- statistics confirm the longrun relationship between the variables and our final model, based on the findings of the shortrun and long-run Wald tests takes the following form:
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In line with previous relevant studies employing the NARDL model (e.g., Jammazi et al., 2015; Katrakilidis $ $ and Trachanas, 2012; Nusair, 2016), the less restrictive case of short-run asymmetry, i.e. ∑/ !.,/ = ∑/ !.,/ , is considered in this study.
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ACCEPTED MANUSCRIPT ∗ ΔGDP∗ = + 567 + 789:;< + 789:;< + = 7>:;< + ? @7: + $
$
∗ A B7 + A B7 + ∑/ / ΔGDP/ + ∑/ !,/ Δ789:;< + ∑/ !,/ Δ789:;< + ∑$/ !=,/ Δ7>:;< + ∑$/ !=,/ Δ7>:;< + ∑$/ !?,/ Δ@7: + ∑$/ !?,/ Δ@7: + $ ∑$/ !A,/ ΔB7 + ∑/ !A,/ ΔB7 + #
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(6)
where 567∗ , 789:;< , 7>:;< , @7: and B7 stands for the non-oil GDP, public and private investment, consumer price index and oil production in period t, respectively and #
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refers to the error term. In turn, 789:;< , 789:;< , 7>:;< , 7>:;< , @7: , @7: , B7 and
variables, respectively.
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B7 are the partial sums of positive and negative changes in each of the explanatory
In the fourth step, the asymmetric cumulative dynamic multiplier effect on of a unit change in and can be derived, respectively, as follows: EFGHI EJGH
, CD = ∑D
EFGHI EJGK
, ℎ = 0,1,2, … …
(7)
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CD = ∑D
Note that as ℎ → ∞ , then CD → and CD → , where and are calculated as = − / and = − /, respectively. As argued by Fousekis et al. (2016), depicting
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and analyzing the paths of adjustment and the duration of the disequilibrium following a positive or a negative shock affecting the system provides useful information on the long-run
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and short-run patterns of asymmetry. We also provide the asymmetric impulse response function and variance
decomposition of our specification as in Equation (7) following Hatemi-J (2011)9.
3.2. Causality in the quantile approach We follow Balcilar et al. (2016) to use the novel nonlinear causality method to examine the causality-in-quantiles between non-oil GDP ( ) and corresponding macroeconomic variables 9
Technical details, not presented here for brevity of space, can be seen in Hatemi-J (2011).
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ACCEPTED MANUSCRIPT ( ). This hybrid nonparametric quantile causality approach is an extension of the work by Nishiyama et al., (2011) and Jeong et al., (2012). Following Jeong et al., (2012)10, we test that does not cause in the -quantile with
regards to the lag-vector of
UV W , … , , , … , " = UV W , … , " However, presumably
causes
in
the
-th
S , … , , , … , T if
with
(8)
regards
to
(9)
UV ( | ∙) is the -th quantile of . The conditional quantiles of , UV ( | ∙)
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where
quantile
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UV W , … , , , … , " ≠ UV W , … , "
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S , … , , , … , T if
depends on t and the quantiles are restricted between zero and one, i.e., 0 < < 1. Let us define vectors a ≡ ( , … , ) , c ≡ ( , … , ) , and d = (c , a ). The functions &FG|eGKf ( |d ) and &FG|gGKf ( |a ), be the conditional distribution functions of conditioned on vectors d and a , respectively. The conditional
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distribution &FG|eGKf ( |d ) is presumed to be completely continuous in for nearly all d . By defining UV (d ) ≡ UV ( |d ) and UV (a ) ≡ UV ( |a ) we can see that
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&FG|eGKf hUV (d )|d i = , which holds with a probability equal to one. Accordingly, the causality-in-quantiles hypothesis based on Equations (8) and (9) can be
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represented as:
j : 7S&FG|eGKf hUV (a )|d i = T = 1
(10)
j : 7S&FG|eGKf hUV (a )|d i = T < 1
(11)
In order to define a measurable metric for the practical implementation of the causality-in-quantiles tests, Jeong et al., (2012) make use of the distance measure l = h# m(# |d )ne (d )i , where # denotes the regression error and ne (d ) denotes the
10
The exposition in this section closely follows Nishiyama et al. (2011) and Jeong et al. (2012).
16
ACCEPTED MANUSCRIPT marginal density function of d . The estimator of the unknown regression error is expressed as: #̂ = ph ≤ UrV (a )i −
(12)
In Equation (9), the quantile estimator UrV (a ) yields an estimate of the -th
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conditional quantile of given a . We estimate UrV (a ) by employing the nonparametric kernel approach as: ( |a) UrV (a ) = &rF G |gGKf
(13)
uGKf KuvKf ∑z x phFv yFG i v{|Hf,v}G st w
uGKf KuvKf ∑z x v{|Hf,v}G st w
(14)
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&rFG|gGKf ( |a ) =
SC
where &rFG|gGKf ( |a ) denote the Nadarya-Watson kernel estimator given by:
with ~(∙) denotes a known kernel function and ℎ is the bandwidth used in the kernel estimation.
Next, we examine the causality-in-variance (2nd moment) because the rejection of
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causality in the moment C does not imply non-causality in the moment for C < , from the macroeconomic variables to the volatility of the non-oil GDP. We can illustrate this by utilizing the following model:
EP
= (c , a ) + #
(15)
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where the higher order causality-in-quantiles can be tested as: j : 7 &F |eGKf hUV (a )|d i = = 1 for = 1,2, … ,
(16)
j : 7 &F |eGKf hUV (a )|d i = < 1 for = 1,2, … ,
(17)
G
G
We test that Granger causes in quantile up to -th moment using Equation
(16) to formulate the feasible kernel-based test statistic following Jeong et al. (2012), for each . For the joint density-weighted nonparametric tests for all = 1,2, … , , we follow the sequential testing approach as in Nishiyama et al. (2011). The lag order of 1 is chosen 17
ACCEPTED MANUSCRIPT based on the Schwarz Information Criterion (SIC) in a VAR model. The bandwidth value is selected by using the least squares cross-validation techniques. Finally, for (∙) and ~(∙) we employ Gaussian-type kernels.
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4. Data and preliminary analysis 4.1. Data
We use quarterly-frequency data for public investment (PUBINV), private investment
SC
(PRINV), consumer price index (CPI) as proxy of inflation, oil production (OP) and non-oil GDP (GDP*). The sample data covers the period ranging from 1992:Q1 to 2014:Q4, totaling
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92 quarterly observations which are widely sufficient for empirical analysis based on the central limit theorem (CLT). The sample data is dictated by their availability and is sourced from different websites: quandl website (https://www.quandl.com/data-sources) and the Central Department of Statistics & Information of Saudi Arabia (http://www.cdsi.gov.sa). The
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choice of oil production is motivated by the fact that rising oil price has been an important factor driving economic growth.
The CPI is the benchmark inflation and plays a key role in affecting the economic
EP
growth as well as investment. We note that there is no persuasive empirical proof for either a positive or a negative relationship between inflation and economic growth. The early part of
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the literature shows a negative impact of inflation on economic growth (Barro, 1995; Fischer, 1983; Valdovinos, 2003), while the most recent compilation of the literature finds a positive effect between inflation and economic growth (Mallik and Chowdhury, 2001; Rapach, 2003; Benhabib and Spiegel, 2009; Pradhan et al., 2015). All the considered variables are crucial for macroeconomics policies (Easterly and Rebelo 1993; Frenkel and Khan 1990; Bleaney 1996; Cordon, 1990; Blomstrom et al., 1996; Madsen, 2002). For empirical analysis, we transform
18
ACCEPTED MANUSCRIPT these variables by taking natural logarithms to correct for potential heteroskedasticity and dimensional differences between the series. Figure 1 plots the trajectory of the considered variables. As shown in this figure, we can observe that non-oil GDP increases along the sample period. More precisely, this variable
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exhibits a significant increase after 2004. The graphical analysis shows that the Saudi government is trying to diversify sectors of the economy to increase the total GDP. Looking at private investment, we can observe that between 1992 and 2005 this economic activity
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variable is relatively stable and exhibits a significant increase after 2008. Between 2008 and 2011, this investment also increases during a period that is marked for the most part by an
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historic increase in the oil price. The CPI exhibits a sharp increase following the 2007/2008 GFC. Oil production decreased in 2003 which corresponds to the 2003 Gulf war and increased in 2008 which corresponds to the dramatic surge in oil prices.
b). Public investment
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EP
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a). Non-oil Gross Domestic Product (GDP*)
c). Private investment
d). Consumer price inflation
19
ACCEPTED MANUSCRIPT
RI PT
e). Oil production
Fig. 1. Time-paths of the quarterly macroeconomic variables.
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Note: Non-oil GDP is in million riyals; public and private investments are in current US$; oil production is in million barrels.
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4.2. Preliminary analysis
Table 1 presents the descriptive statistics for non-oil GDP, private investments, public investment, consumer price index (CPI) and oil production. As shown in this table, the quarterly value of public investment is significantly higher than their private investment
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counterpart throughout the period. This anecdotal result indicates that the Kingdom should encourage and promote the private sector and there is ample room for that. The mean value of non-oil GDP is equal to SR 683,896.2 million and ranges between a maximum value of
EP
SR1,653,776 and a minimum of SR302,903.8. More importantly, all series deviate from the normal distribution as indicated by the Jarque-Bera test with the exception of oil production.
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This stylized fact is a source of nonlinearity in our series. The unconditional correlation results among our variables are reported in Table 2. The non-oil GDP growth is positively correlated with both private and public investments. This result indicates that both variables move in the same direction. In addition, oil production is positively related to non-oil GDP, meaning that increasing oil production improves not only oil-GDP but also non-oil GDP growth.
Table 1: Descriptive statistics 20
ACCEPTED MANUSCRIPT PUBINV 737829.3 393449.5 1958955 245236.3 550005.3 0.858441 2.254328 13.43088 [0.0012] 92
PRINV 100119 31396.92 334369.9 5051.903 111689 0.902811 2.120711 15.46144 [0.0004] 92
CPI 85.23716 78.28883 116.2507 72.11067 13.4081 1.140853 2.746759 20.20286 [0.0000] 92
OP 3114.989 3024.074 3588.523 2552.986 265.4633 0.202538 2.133932 3.504281 [0.1734] 92
RI PT
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability No. of Obs.
GDP* 683896.2 471362.2 1653776 302903.8 400131.9 1.018725 2.726316 16.20006 [0.0003] 92
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Note: Data is quarterly, ranging from 1992 until 2014. Non-oil GDP is in million riyals. Public investment is proxied by gross fixed capital formation (current US$) in millions, private investment is the difference between total investment and public investment. CPI is a gauge of consumer price inflation, and oil production is Saudi Oil Production (in million barrels).
PRINV
CPI
1.0000
0.8998*** (19.562) [0.0000] 0.9623*** (33.552) [0.0000] 0.7611*** (11.130) [0.0000]
PRINV
CPI
OP
1.0000
0.8822*** 1.0000 (17.772) [0.0000] 0.5382*** 0.6597*** (6.0573) (8.3285) [0.0000] [0.0000]
1.0000
EP
OP
PUBINV
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GDP* PUBINV
GDP* 1.0000 0.9926*** (77.457) [0.0000] 0.8736*** (17.027) [0.0000] 0.9743*** (41.022) [0.0000] 0.7437*** (10.553) [0.0000]
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Table 2: Correlation matrix for the variables
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Note: This table presents the unconditional correlation among non-oil GDP, private investment, public investment, oil production and CPI. The values in parenthesis are the standard deviations, while those in brackets are the p-values. *** indicates significance at the 1% level.
5. Empirical results
We first establish the unit root properties of the data using the Narayan and Popp
(2010) unit root test, which indicates two structural breaks at unknown locations in the deterministic components of the series. Notably, the standard unit root tests have a low power when the time series exhibit structural breaks over time. One of the main advantages of the Narayan and Popp (2010) unit root test is that it does not require an a priori knowledge about
21
ACCEPTED MANUSCRIPT the timing of possible structural breaks because the break dates are endogenously determined within the model. Another distinctive feature of this test is that breaks are allowed under both the null and alternative hypotheses. Two different specifications are considered by Narayan and Popp (2010) to test the order of integration of a series. Based on the Monte Carlo
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simulations, Narayan and Popp (2013) have shown that this unit root test has better size and power properties and identifies the breaks more accurately than its main two-break unit root rivals, namely the older Lumsdaine and Papell (1997) and the Lee and Strazicich (2003) tests.
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Since the Narayan and Popp (2010) unit root test has already been widely employed in the economic and financial literature, we do not reproduce the details of this test.11
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Table 3 reports the results of the Narayan and Popp (2010) unit root test with two structural breaks. Specifically, model M1 allows two structural breaks only in the level, while model M2 permits two breaks both in the level and the trend of a time series. The results reveal that the variables have a mixed order of integration where few are stationary in the
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level, while others are stationary in the first difference. The most important finding of the Narayan and Popp (2010) unit root test is that none of the variables is found to be I(2). Furthermore, the presence of structural breaks in the time series data gives an early indication
EP
of a asymmetric behavior of the time series over time, and hence underscores the possibility of asymmetric short- and long-run relationships between the variables. The break dates can be
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linked to global economic and political events. The dates of structural breaks for private investment correspond to the 2003 Gulf war which involved the occupation of Iraq. The break dates for other variables also coincide with some significant historic events like the Asian financial crisis of 1997-1998 and the GFC of 2007-2008. Breaks in the time series data with mixed order of integration require the use of the asymmetric bound-testing approach as proposed by Shin et al. (2014) since this approach 11
For a detailed description of the methodology of the Narayan and Popp unit root test, see for example Narayan and Popp (2010 and 2013).
22
ACCEPTED MANUSCRIPT relaxes the usual assumption used in the cointegration analysis which requires that all variables must be integrated of the same order.
Table 3: Results of the Narayan and Popp (2010) unit root test with two structural breaks
TB2
k
1997Q4 2008Q1 2000Q4 1998Q2 1998Q4
2003Q4 2008Q4 2003Q1 2007Q3 2001Q4
1 5 5 4 5
1996Q4 2008Q1 2000Q4 2007Q2 1998Q4
2003Q1 2008Q4 2003Q1 2008Q4 2001Q4
4 4 4 0 4
Test Statistics
TB1
TB2
k
-0.610 0.588 -4.019* -1.189 -4.641*
1997Q4 2008Q1 2000Q4 2007Q3 1998Q4
2003Q4 2008Q4 2003Q1 2010Q1 2001Q4
1 5 5 0 6
-5.489*** -4.898* -5.140** -5.447** -5.837***
1997Q3 2008Q1 2000Q4 2006Q3 2001Q4
2003Q4 2008Q4 2004Q1 2007Q4 2007Q4
4 4 4 0 4
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Level lnGDP* -3.356 lnPUBINV -0.078 lnPRINV -4.014* lnCPI -4.468** lnOP -3.846 First Difference lnGDP* -4.943** lnPUBINV -5.978*** lnPRINV -4.164* lnCPI -4.318** lnOP -5.497***
TB1
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Test Statistics
Model M2
RI PT
Model M1
EP
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Note: This table displays the results of the Narayan-Popp unit root test for models M1 and M2 as explained in Narayan and Popp (2010). Model M1 (M2) assumes two structural breaks at unknown dates in the level (level and slope) of each series. The test statistics for the null hypothesis of a unit root are presented for both the series in the level. The critical values for model M1 are -4.988, -4.316 and -3.980 at the 1%, 5% and 10% significance levels, respectively. The critical values for model M2 are -5.576, -4.937 and -4.596 at the 1%, 5% and 10% significance levels, respectively. These critical values have been collected from Narayan and Popp (2010) based on 50,000 replications for a sample size of 50 observations. TB1 and TB2 are the dates of the structural breaks selected according to the sequential procedure discussed in Narayan and Popp (2010) and k stands for the optimal lag length obtained by using the procedure suggested by Hall (1994) and Narayan and Popp (2010). Following Narayan and Popp (2010), a trimming percentage of 20 is used. As usual, the asterisks ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
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The presence of long-run asymmetric linkages between the Saudi public investment, private investment, oil production, CPI and non-oil GDP is confirmed using the bound-testing procedure. The results summarized in Table 4 also show a nonlinear long-run relationship between the non-oil GDP and the explanatory variables. The long- and short-run asymmetries of each variable are examined using the Wald test. The results indicate that public investment and oil production have a long-run asymmetric impact on non-oil GDP, whereas the impact of private investment and CPI is symmetric in the long-run. This result may be explained by the fact that public investments are based on oil revenues. In addition, the oil market is marked by 23
ACCEPTED MANUSCRIPT strong asymmetry behavior which is due to geopolitics and OPEC decisions (Mensi et al., 2014). As for the short-run, we find strong evidence that all the variables have an asymmetric short-run association with the non-oil GDP.
Statistic 7.3713*** -5.5623*** 6.4230** 37.540*** 2.0760 4.3950** 0.5374 2.9620* 62.610*** 3.6360*
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FPSS *+,WLR(PUBINV) WSR(PUBINV) WLR(PRINV) WSR(PRINV) WLR(CPI) WSR(CPI) WLR(OP) WSR(OP)
p-value [0.0130] [0.0000] [0.1570] [0.0390] [0.4670] [0.0890] [0.0000] [0.0600]
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Tests
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Table 4: Results of the bound tests and the Wald tests for long-run and short-run asymmetry Decision Cointegration Cointegration Asymmetry Asymmetry Symmetry Asymmetry Symmetry Asymmetry Asymmetry Asymmetry
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Note: This table reports the results of the bounds-testing procedure for cointegration in the non-linear ARDL (NARDL) models and the Wald statistics of the long- and short-run symmetry tests for each country. The exact specification and the results of the NARDL models are presented analytically in Table 4. The statistics FPSSNonlinear and tBDM denote the F-statistic and the t-statistic proposed by Pesaran et al. (2001) and Banerjee et al. (1998), respectively, for testing the null of no cointegration in the NARDL model. The critical values for these statistics have been obtained from Pesaran et al. (2001). WLR denotes the Wald statistic for the long-run symmetry, which tests the null hypothesis of = for each explanatory variable in Eq. (7). WSR corresponds to the Wald statistic for the short-run asymmetry, $ $ which tests the null hypothesis that ∑/ !.,/ = ∑/ !.,/ for each explanatory variable in Eq. (7). ***, ** and * indicate a rejection of the null hypothesis of no cointegration and symmetry at the 1%, 5% and 10% levels, respectively.
EP
Based on the obtained results of the bounds tests and the Wald tests for the long-run and the short-run asymmetry and the confirmation of cointegration among the considered
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variables, it is now legitimate to use the NARDL model. Table 5 reports the dynamic asymmetric estimation of the relationship between the non-oil GDP and the macroeconomic variables. Looking at the short-run results, we find that past non-oil GDP shocks affect current non-oil GDP. In fact, the first lag quarter non-oil GDP significantly increases the next quarter non-oil GDP as the short-run lagged variable is statistically significant. On the other hand, previous positive public investment shocks have a positive impact on non-oil GDP. This result is explained by the fact that the Saudi government invests in the non-oil sector by
24
ACCEPTED MANUSCRIPT building large projects such as petrochemicals. Moreover, the non-oil GDP is dominated by the service sectors which benefit from higher income in the economy. These results are not in line with Fasano and Wang (2001) who fail to provide evidence of a causality running from public expenditures to non-oil GDP in the GCC countries.12 Also, negative private investment
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shocks increase non-oil GDP in the short-run. We should note that the oil sector grew by 5.1% year-over-year in the first quarters of 2016, but the non-oil sector shrank by 0.7% which is the weakest reading in at least five years. The non-oil GDP is highly sensitive to
SC
uncertainties. This weedy statistic should motivate the Saudi government to move forward with its plan to diversify its economy away from the oil sector. More interestingly, CPI and
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oil production shocks have a negative impact on non-oil GDP in the short-run. The latter effect may have to do with the behavior of oil prices or the Dutch Disease. Regarding the long run, a positive public investment shock (L ) significantly increases (0.369) non-oil GDP in the long-run, whereas a negative public investment shock
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(L) does not impact the variable. This result has to do with the underlying characteristic of this huge oil exporter that uses foreign reserves and fiscal policy to stabilize its economy. Private investment does not impact non-oil GDP in the long-run, which may be due to its
EP
small size relative to that of public investment or it is possible there is a crowding out by
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public investment. On the other hand, CPI has a significant positive (0.798) impact on non-oil GDP which could be a reflection of higher oil prices. Finally, a positive shock in oil production (L) significantly increases (0.399) non-oil GDP because of the connection with the oil sector, while a negative oil production shocks (L) significantly reduces (-0.455) it. It is worth noting that the Saudi government consumption depends on oil production which yields its oil revenues.
12
The included Gulf countries are all GCC countries with the exception of Kuwait.
25
ACCEPTED MANUSCRIPT Table 5: Dynamic asymmetric estimations
0.8708 0.369 [0.000] -0.017 [0.140] 0.399 [0.000] 1.483 [0.122] -629.2929
Adjusted R-squared L L± L = BIC
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R-squared L L± L = ( AIC
T-ratio [p-value] 5.57 [0.000] -5.56 [0.000] 4.35 [0.000] 0.30 [0.765] -1.41 [0.164] 4.69 [0.000] 3.21 [0.002] -3.51 [0.001] 2.15 [0.035] 6.13 [0.000] 2.10 [0.039] -1.72 [0.089] -1.91 [0.060]
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Constant ∗ ln GDP ln PUBINV ln PUBINV ln PRINV ln CPI ln OP ln OP Δln GDP ∆ln PUBINV ∆ln PRINV= ∆ln CPI? ∆ln OP
Standard error 0.3213 0.0254 0.0119 0.0187 0.0017 0.0240 0.0175 0.0183 0.0822 0.0366 0.0056 0.1400 0.0408
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∆ln GDP∗ Coefficients 1.7913*** -0.1413*** 0.0522*** 0.0056 -0.0024 0.1127*** 0.0563*** -0.0643*** 0.1767** 0.2248*** 0.0119** -0.2410* -0.0778*
Dependent variable :
0.8386 0.040 [0.765] 0.798 [0.000] -0.455 [0.000] 0.492 [0.415] -517.8127
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Note: This table reports the results of the estimation of the best-suited NARDL model for the adjustment of the non-oil GDP. The superscripts “+” and “-” denote the positive and negative partial sums, respectively. ~J and ~J are the estimated long-run coefficients associated with positive and negative changes of the variable x, respectively, defined by ~r = − r. Adj. R2 represents the value of the adjusted R2 coefficient of the estimated
= model. ( and = denote the LM tests for serial correlation and heteroscedasticity, respectively. The *** ** superscripts , and * indicate the 1%, 5% and 10% significance levels, respectively. The standard errors and the p-values are in parentheses and brackets, respectively.
EP
Figure 2 plots the evolution of the adjustments of the cumulative dynamic multipliers
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derived from the NARDL model for the non-oil GDP with respect to each explanatory variable (private investment, public investment, oil production and CPI). These multipliers display the patterns in which non-oil GDP adjusts to its new long-term equilibrium following a negative or a positive unitary shock in public investment, private investment, CPI and oil production. The estimated dynamic multipliers are based on the best-fitting NARDL model selected by the Akaike information criterion. The positive (dashed green line) and negative (dashed red line) changes capture the adjustment of the non-oil GDP to positive and negative shocks in the variables under discussion at a given forecast horizon of 40 quarters. The 26
ACCEPTED MANUSCRIPT asymmetric curve (continuous blue line) represents the difference between the dynamic multipliers associated with positive and negative shocks i.e.,
m h+ − m h−
. This curve is displayed
along with its lower and upper bands (blue background) at the 95% confidence interval and presents a measure of the statistical significance of asymmetry at any horizon h. non-oil GDP to a unitary positive change in public
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The adjustment pattern of
investment is positive with a significant asymmetry more towards positive shocks probably because of small crowding out and the government’s active stabilization policy in the face of
SC
bad shocks. The non-oil GDP also achieves a new equilibrium within approximately one year (i.e., within a four-quarter time period) of the occurrence of a shock in public investment. In
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contrast, the effect of positive and negative private investment shocks on the non-oil GDP is symmetric and negative over long-run; however, it is asymmetric in the short-run. The CPI symmetric (asymmetric) shocks increase non-oil GDP in the long-run (short-run). A positive (negative) shock in oil production asymmetrically increases (decrease) the non-oil GDP in the
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long-run. Overall, the non-oil GDP achieves a new equilibrium point within 10 quarters after a unitary shock to a particular explanatory variable such as public investment.
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EP
Figure 2: Dynamic multipliers from the NARDL model for each variable
27
ACCEPTED MANUSCRIPT Cumulative effect of PRINV on GDP*
0
10
20 Time periods
30
40
0
10
20 Time periods
positive change
negative change
positive change
asymmetry
CI for asymmetry
asymmetry
Note: 90% bootstrap CI is based on 100 replications
30
40
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0
.1
-.02 -.01
.2
0
.3 .4
.5
.01 .02
Cumulative effect of PUBINV on GDP*
negative change
CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of OP on GDP*
-1
-.5
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-.5
0
0
.5
.5
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1
1
Cumulative effect of CPI on GDP*
0
10
20 Time periods
30
40
0
10
20 Time periods
30
40
positive change
negative change
positive change
negative change
asymmetry
CI for asymmetry
asymmetry
CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Note: 90% bootstrap CI is based on 100 replications
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Notes: These figures display the dynamic multipliers showing the pattern of adjustment of the non-oil GDP (GDP*) to the new long-run equilibrium following a negative or positive unitary shock in each of the explanatory variables. The dynamic multipliers have been estimated based on the best-suited NARDL model reported in Table 5. The vertical axis shows the asymmetric coefficients, while the horizontal axis indicates the time periods in quarters.
Next, we present the asymmetric generalized impulse response (AGIR) functions and
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asymmetric variance decompositions (AVD), following Hatemi-J (2011) with the same specifications as in Equation (7). The impulse response functions are used to determine the propagation process of a shock over time (Sims, 1980). A VAR model that treats all variables in the model endogenously is used.13 Figure 3 demonstrates the response of the non-oil GDP (GDP*) to the variables represented in the cumulative positive/negative changes. It can be clearly seen that the response of non-oil GDP to the explanatory variables is in line with our 13
To honor space limitations, we do not provide the technical details of the asymmetric impulse response functions and asymmetric variance decompositions. These details can be found in Hatemi-J (2011); however, the positive and negative cumulative sums of the variables are the same as used for the NARDL estimation.
28
ACCEPTED MANUSCRIPT findings of the NARDL model, thus the AGIR can be considered as a robustness test to the previous dynamic multipliers of the NARDL model.
Response of GDP* to PUBINV (+)
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Figure 3: NARDL impulse response to generalize one standard deviation innovation ± standard error Response of GDP* to PRINV
Response of GDP* to PUBINV (-) .008
.008
.006
.006
.006
.004
.004
.004
.002
.002
.002
.000
.000
.000
-.002
-.002
-.002
-.004
-.004 2
3
4
5
6
7
8
9
-.004
1
10
2
3
.008
.008
.006
.006
.004
.004
.002
.002
.000
.000
-.002
-.002
3
4
5
6
7
8
6
7
8
9
10
1
2
9
10
3
4
5
6
7
8
9
10
Response of GDP* to OP (-)
.008 .006 .004 .002 .000
-.002 -.004
-.004
-.004 2
5
Response of GDP* to OP (+)
Response of GDP* to CPI
1
4
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1
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.008
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
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Notes: These figures show the asymmetric generalized impulse response functions of the non-oil GDP (GDP*) for each of the explanatory variables. These GIRFs are obtained through VAR using the specifications of the best fitted model reported in Table 5. The vertical axis is expressed in units of non-oil GDP (GDP*). The solid blue line is a point estimate for the quantity GDP* expected to change following a unit impulse over the number of periods (in quarters) on the horizontal axis.
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Table 6 reports the results of the asymmetric variance decompositions (AVD) for
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non-oil GDP (GDP*) with respect to the four explanatory variables. The non-oil GDP explains 79.37% of the variation in its own forecast errors over a 16-quarter time horizon. The positive and negative shocks of the public investment explain around 3.89% and 2.03% of the variation in the forecast error of non-oil GDP over the same time horizon. The CPI explains 4.63% of the variation in the forecast error of the non-oil GDP, whereas the negative oil production shocks explain around 8.70% of these variations in the non-oil GDP over the 16 quarters. These findings are in line with the estimation results of the NARDL model.
29
10
ACCEPTED MANUSCRIPT However, we test the validity of the NARDL model through the application of AGIR and AVD.
Table 6: Asymmetric Variance Decomposition of GDP* GDP* 100.000 97.633 96.576 95.611 90.606 84.888 79.368
PUBINV+ 0.000 0.584 0.653 0.638 1.103 2.410 3.895
PUBINV0.000 1.597 2.067 2.240 2.275 2.121 2.027
PRINV 0.000 0.052 0.202 0.397 0.856 0.803 0.995
CPI 0.000 0.053 0.220 0.495 2.131 3.625 4.632
OP+ 0.000 0.017 0.066 0.137 0.378 0.422 0.386
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S.E. 0.006 0.006 0.007 0.007 0.007 0.007 0.008
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Period 1 2 3 4 8 12 16
OP0.000 0.064 0.216 0.482 2.651 5.732 8.698
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Note: This table presents the variance decomposition of non-oil GDP (GDP*).
We close the empirical result section by using a novel technique namely the causalityin-quantiles to examine the causality-in-mean (first order) and the causality-in-variance (second order) between non-oil GDP and the four explanatory variables. Since, this method requires stationary time series, we first-difference the time series for this application. It is
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worth noting that our variables exhibit structural breaks and show an asymmetric relationship, which justifies the recourse to the nonparametric causality-in-quantile approach.
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The results are plotted in Figure 4. Looking at the causality from the explanatory variables to non-oil GDP, we observe that public investment Granger causes in-mean and in-
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variance non-oil GDP across the quantiles with the exception of the highest and lowest quantiles. The null hypothesis of no Granger causality-in-mean from public investment to non-oil GDP is rejected at the usual levels of significance over the entire conditional distribution of the non-oil GDP returns, suggesting a predictability for the public investment. More specifically, public investment Granger causes in mean (in variance) the non-oil GDP for the quantiles ranging from q=0.15 to q=0.85 (0.1 to 0.9), suggesting little crowding out if any by public investment. Additionally, we find little evidence that private investment
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ACCEPTED MANUSCRIPT Granger causes non-oil GDP, probably due to its small share in an oil economy that depends heavily on government spending. Concerning oil production, this variable Granger-causes in-mean and in-variance non-oil GDP for the majority of the quantiles. However, we accept the null of no Granger
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causality-in-mean and in-variance for the CPI-non-oil GDP pair. By looking at the trajectories for the causality-in-quantiles from non-oil GDP to the other macroeconomic variables, we accept the null hypothesis of no Granger-causality for all pairs except for the oil production
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oil production for the quantiles q=0.35 and q=0.4.
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variable. More precisely, we find a significant Granger causality-in-mean and in-variance for
Figure 4: Non-parametric causality-in-mean and variance at various quantiles GDP* -/-> PUBINV
GDP* -/-> PRINV
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PRINV -/-> GDP*
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PUBINV -/-> GDP*
CPI -/-> GDP*
GDP* -/-> CPI
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GDP* -/-> OP
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OP -/-> GDP*
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Note: The figure plots the estimates of the nonparametric causality tests of the various quantiles. The y-axis reports the test statistics, while the quantiles are placed on the x-axis. The horizontal black solid lines represent the 5% critical values (CV).
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6. Conclusions and policy implications
Investment is one of the most important and fundamental sources of economic growth. More explicitly, both public and private investments usually play a vital role in increasing
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capacity and production of goods and services in all economies and they can be complementary to each other. Over the recent years, the successive financial crises including
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the global financial crisis of 2008-2009 and the European sovereign-debt crisis of 2010-2011 have curtailed investment and economic growth and incapacitated fiscal policy as a tool of providing public investment. The recent plunges in oil prices have strained the resources of oil-exporting countries and hindered their ability to enhance public investment, expand output capacity and foster economic growth. This study analyzes the effects of private investments, public investments and other macroeconomic and oil variables on
non-oil GDP of Saudi Arabia which has several non-
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ACCEPTED MANUSCRIPT standard investment and inflation characteristics, and thus stands to provide unusual and interesting results than is the case in well-diversified developed countries. To demonstrate the economic relevance of our empirical findings, this study adopts two methods. First, it applies the asymmetric nonlinear autoregressive distributed-lag
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(NARDL) model, with positive and negative partial sum decompositions of the macroeconomic and oil variables, to the Saudi non-oil GDP and inflation to harness the longrun information contained in the levels of the variables under consideration and to avoid
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having a spurious regression at the same time. This model has advantages over the conventional approach of Johansen and Juselius (1990), including its practical suitability
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irrespective of whether the underlying regressors are I(0), I(1) or mutually cointegrated. We also derive the impulse response function for non-oil GDP using this model. Second, we apply the nonparametric causality-in-quantile method to assess the relationship between the variables across the quantiles. More precisely, we investigate this relationship using both the
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nonparametric causality-in-mean and the nonparametric causality-in-variance to analyze the impact of public and private investments, oil production and inflation on non-oil GDP. Several major conclusions may be drawn from this analysis. Using the Narayan and
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Popp (2010) unit root test, all macroeconomic variables exhibit significant structural breaks, which coincide with important events, such the recent global financial crisis, and geopolitical
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factors. Furthermore, we find evidence of a significant nonlinear relationship between nonoil GDP and the other macroeconomic variables (public investment, private investment, CPI and oil production), highlighting that the relationship between these variables may differ in the short-and long-run, which has relevance for policy makers in the short run and development planners in the longer run. In the short-run, by using the NARDL model, we find that past non-oil GDP shocks considerably affect current non-oil GDP, underscoring the dynamic steadiness of non-oil
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ACCEPTED MANUSCRIPT economic growth. Further, previous positive public investment shocks increase the non-oil GDP, suggesting very little crowding out if any, which comes in contrast to the traditional investment theory for developed countries. Interestingly, negative private investment shocks increase
non-oil GDP in the short-run, underlying the importance of public investment
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compared to private investment and the compensating role the Saudi government takes to stabilize the economy. It may also imply that the private sector increases its production efficiency to counter adverse shocks. More interestingly, the negative CPI shocks and
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negative (positive) oil production shocks have a negative (positive) impact on non-oil GDP in the short-run, which could be due to heightened uncertainty in the economy caused by
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those shocks. The oil-induced Dutch Disease may also be alive in Saudi Arabia in the shortrun.
In the long run, a positive public investment shock significantly increases the non-oil GDP, again suggesting little crowding out in the long run, whereas a negative public
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investment shock does not impact much the non-oil GDP, probably due to the government’s stabilization policy supported by a huge sovereign wealth fund which is used to fend off political pressure. On the other hand, private investment does not impact non-oil GDP in the
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long-run, again due to its small contribution to this heavily oil-based economy, while both negative and positive CPI shocks have a significant positive impact in the long run because of
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the importance of imports of goods and services in the short-run. A positive shock in oil production significantly increases non-oil GDP of the world’s largest oil exporter in the long run, while a negative oil production shock reduces it. This has policy implications pertinent to how the Saudi government governs its oil policy in OPEC. Using the nonparametric causality-in-quantile framework, we find that past public investment Granger causes in-mean and in-variance non-oil GDP across the quantiles, with the exception of the highest and lowest quantiles, confirming the corresponding “crowding
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ACCEPTED MANUSCRIPT in” results of the NARDL approach. Moreover, we find little evidence that private investment Granger causes non-oil GDP, emphasizing the small importance of this economic activity in the Saudi Economy, which is also consistent with the corresponding results of the NARDL model. Oil production Granger-causes in mean and in variance non-oil GDP for almost all
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quantiles. For CPI, we find no Granger causality in mean and in variance of the non-oil GDP. In contrast, non-oil GDP does no Granger-cause the other macroeconomic variables as is the case with the NARDL results, underlying the importance of oil exports proceeds in propelling
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the Saudi economy.
These results have several important implications for policymakers and institutional
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investors dealing with the Saudi economy. The investment horizon is a key factor for determining the controversial relationship between inflation and non-oil GDP. In fact, inflation changes from a negative relationship in the short term to a positive relationship in the long term, which confirms the nonlinearity in this nexus. The threshold time is a
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determinant variable for policy makers to establish their short- and long-term strategies to deal with increasing inflation, which is important for the Saudi planning initiative, Vision 2030. Low inflation rates should minimize uncertainties in the financial markets, which in
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turn boosts investment. We should note that the Saudi inflation rate reached 34% and 8% in 1973 and 2008, respectively, and those two years coincide with strong oil booms. In fact, we
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can argue that not only high oil prices and production affect inflation but also monetary expansion which brings in imported inflation due to the effective and actual pegged currency policies, growth in domestic demand and increases in food prices and rental prices which can be considered among the main drivers of inflation in the GCC. Oil production is crucial for oil revenues for Saudi Arabia. In fact, a great part of the total GDP comes from the oil GDP. The negative sign of private investment may be an indication of a crowding out effect on economic growth or a sign of the Dutch Disease in
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ACCEPTED MANUSCRIPT Saudi non-oil GDP in the long run as a result of resource movements. There is also evidence to support the theory of a long-run "crowding-out" effect of public investment. Along this line, we can dare to underscore the role of public investment and oil production to improve the non-oil GDP. The Saudi government remains an oil-dependent economy despite the
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previous diversification efforts to diversify. Its Vision 2030 aiming to diversify away from oil will face major challenges.
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Acknowledgments
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The first author (W. Mensi) greatly appreciates the financial support provided by Scheikh AlFawzan Research Chair for Expectations of Saudi Macro-economy at Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
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Highlights
• We study the impact of the macroeconomic variables on non-oil GDP in Saudi Arabia.
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• We use the nonlinear ARDL and the non-parametric causality-in-quantile methods. • A nonlinear relationship between the macroeconomic variables and the non-oil GDP is found.
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• There is evidence of causality-in-mean and -in-variance emanating for macroeconomic variables.
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• These relationships depend on the time horizons (short- and long-run) and the quantiles.
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S2110-7017(17)30090-2
DOI:
10.1016/j.inteco.2017.10.003
Reference:
INTECO 141
To appear in:
International Economics
Received Date: 24 April 2017 Revised Date:
19 October 2017
Accepted Date: 19 October 2017
Please cite this article as: Mensi, W., Hussain Shahzad, S.J., Hammoudeh, S., Al-Yahyaee, K.H., Asymmetric impacts of public and private investments on the non-oil GDP of Saudi Arabia, International Economics (2017), doi: 10.1016/j.inteco.2017.10.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Asymmetric impacts of public and private investments on the non-oil GDP of Saudi Arabia Walid Mensia,b , Syed Jawad Hussain Shahzadc, Shawkat Hammoudeh c,d, Khamis Hamed AlYahyaeeb a
Lebow College of Business, Drexel University, Philadelphia, United States Email: [email protected]
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d
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Energy and Sustainable Development (ESD), Montpellier Business School, Montpellier, France Email: [email protected]
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Abstract This paper investigates the impact of four major macroeconomic variables (private investment, public investment, oil production and inflation) on non-oil GDP in the oil-based Saudi Arabia. To this end, we use the nonlinear ARDL (NARDL) and the causality-in-quantiles methods to measure the impact of these variables on non-oil GDP. The results show that past non-oil GDP shocks affect current non-oil GDP strongly in short term. Moreover, a surge in public investment increases non-oil GDP in both the short- and long-run, while a negative private investment shock reduces non-oil GDP in both the short- and long-run. Furthermore, positive (negative) oil production shocks increase the nonoil GDP also in the short- and long-run. In addition, we find a positive relationship between negative and positive inflation shocks and non-oil GDP in the long run, while negative inflation shocks decrease non-oil GDP. Using the nonparametric causality-in- quantile approach, we find that causality-in-the mean and causality-in-the variance emanating from the four explanatory variables vary across the quantiles. Finally, non-oil GDP does not Granger cause these macroeconomic variables. Those non-standard macroeconomic results for this major oil exporter are different from those for well-diversified developed countries. Regardless, they have important implications for Saudi policy makers involved in the Vision 2030 initiative, international organizations and institutional investors.
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c
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Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman Email: [email protected] Email: [email protected]
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b
Department of Finance and Accounting, University of Tunis El Manar, Tunis, Tunisia
JEL classification: G14; G15 Keywords: Saudi Arabia; Investments; Macroeconomic variables; non-oil GDP; NARD; Causality-in-quantiles.
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ACCEPTED MANUSCRIPT 1. Introduction Investment and economic growth are two important macroeconomic variables that are strongly related in all economies. They are interconnected since investment (public or private) is a major pillar for growth in any economy. Investment refers to any economic activity that
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involves providing and combining resources to produce goods and services, leading to more output and greater economic growth. Putting crowding out aside, private and public investments are likely complementary economic activities that harness greater resources for
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producing more output and increasing gross domestic product (GDP). Dreger and Reimer (2016) provide evidence that shows that a lack of public investment may restrict private
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investment and arrest GDP growth in the euro area. Public investment through active fiscal policy raises output in both the short- and long-term and in most cases “crowds in” private investment in advanced economies (Abiad et al., 2015).
Investment decisions are widely related to major economic and political events or
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phases of the business cycle, as well as to the volatility of commodity markets like the crude oil market for both producing and consuming countries. The recent global financial crisis (GFC) of 2008-2009 and the European sovereign-debt crisis (ESDC) of 2010-2012 have put
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financial constraints on governments and affected investment decisions and economic growth (Barbosa et al., 2016). Among the countries that are affected by these events are the oil-rich
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member countries of the Gulf Cooperation Council (GCC) which had experienced a drastic drop in oil revenues as a result of the recent collapse of oil prices, following the historic increase in summer 2008. The oil price is a key propeller of investment and economic growth for Saudi Arabia, the world’s largest oil exporter. In fact, the Kingdom is the largest economy in the GCC, Middle East and North Africa (MENA) regions, the second largest global oil producer and its oil and non-oil GDP depends mainly on public investment which fuels economic growth. Further, the Saudi government’s revenues are significantly based on the
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ACCEPTED MANUSCRIPT proceeds from oil exports. The Saudi government’s spending is equally dependent on oil revenues.1 More importantly, oil prices dropped in June 2014, leading to lower investment and slower economic growth in the country. On the other hand, inflation in the GCC countries has moved in concordance with
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public investment and oil prices in those years.2 The inflation in Saudi Arabian, in particular, is determined through the cost of living Index (CLI) and the wholesale price index (PCI).3 The Saudi economy is based on public investment and public/private consumption
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which are the major drivers of its total GDP. The Saudi public investment is based particularly on the oil sector activity. As we indicated above, after a surge in prosperity over
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the last years which was fueled by rising oil prices, the Saudi economy is at an inflection point. In fact, the recent plunges in oil prices have pushed the Saudi government to introduce substantial reforms in order to wane its economy off oil revenues, strengthen the non-oil GDP and foster the private sector which is dominated by a handful of big businesses in the services
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subsectors. 4 One of the main objectives of the Saudi government in its Vision 2030 is to develop a well-diversified economy by 2030.
This paper purposes to examine the impact of private and public investments, oil
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production and inflation on non-oil GDP in Saudi Arabia. We focus on the Saudi non-oil GDP
1
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because of the recently developed Vision 2030 of the Kingdom which aims to diversify its According the BP Statistical Review of World Energy (June 2015), the total oil reserves of Saudi Arabia were 267.0 billion barrels at the end of 2014 and its total oil production reached 11.701 million barrels/day in that period. Its real GDP had risen from Saudi Riyal (SAR) 863.5 billion at the end of Q1:1990 to SAR 2,464.3 billion at the end of Q4:2014, a 284.5% increase. Its real fixed investment was SAR 182.7 billion at the end of 1990 and is expected to reach SAR 731.4 billion near the end of 2018. The government (private) consumption increased from SAR 188.7 (SAR 290.1) billion in 1990Q1 to SAR 582.6 (802.4) billion at the end of 2014 and is expected to reach SAR 637.1 (933.3) billion at the end of 2018.1 Note that a large part of the total GDP (i.e., more than 60%) of Saudi Arabia is drawn from the public sector. These statistics are sourced from the IHS Global Insight. 2 Based on the World Development Indicators statistics, CPI changed between 1990 and 2014 from 0.929 to 2.651 for Bahrain, 9.833 to 2.908 for Kuwait, 3.000 to 3.082 for Qatar and 2.0771 to 2.6700 for Saudi Arabia. More recently, inflation rates vary among the GCC member countries, ranging between 0.4% for Oman and 3.67 % for UAE by end of January 2015 over the previous year. For further information on GCC inflation, the reader can visit http://gccstat.org/en/about/news/january-inflation-2015. 3 https://www.samba.com/.../Inflation_in_Saudi_Arabia_Eng.pdf. 4 For further information on Vision 2030, the reader may use the following link: vision2030.gov.sa/download/file/fid/417.
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ACCEPTED MANUSCRIPT economy, encourage private investments and stimulate non-oil GDP. Th objective of this vision plan is to diversify revenues and avoid depending on oil proceeds as the major economic pillar of the Kingdom. In fact, there is a general trend among the GCC oilexporting countries to focus more on non-oil GDP, and Saudi Arabia is the leader in this
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regard. The choice of the considered exogenous variables in the current study can be explained by their strong linkages to the Saudi economy as discussed above.5 As indicated earlier, the private and public investments can be complementary in an oil-exporting,
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developing economy such as that of Saudi Arabia where the latter “crowds in” the former, which is different of what we may observe in well diversified developed countries most of the
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time. We have also selected the inflation variable because mild and stable inflation makes it easier for businesses in the country to make better investment decisions and for wages to rise steadily and stimulate demand (Pradhan et al., 2015).
For this end, we use the nonlinear autoregressive distributed lag (NARDL) model and
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the nonparametric causality-in-quantile approach. These methods are robust and suitable to nonlinearity that is usually dominant in the data, which is caused by asymmetry and the presence of structural breaks in the economic time series. The NARDL model is generally an
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asymmetric extension of the linear ARDL approach, which is designed primarily to model the long-run relationships among the variables of interest. In our case, we have chosen the
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NARDL model because our time series most likely have ‘hidden cointegration’ and the positive and negative components of the series are cointegrated due to their connections to oil prices which have experienced major structural breaks (Granger and Yoon, 2002; Raza et al., 2016). Shin et al. (2014) derived the dynamic error-correction representation related to the asymmetric long-run cointegration regression, resulting in the NARDL model. Moreover, 5
More interestingly, these variables are widely studied in the previous literature (see the next section) but are not used to investigate their impact on the non-oil GDP.
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function and asymmetric variance decomposition (AVD) approaches. In fact, we argue that this asymmetric framework may be used as a robustness test of the asymmetric dynamic estimations.
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As for the nonparametric causality-in-quantile method, this approach allows one to investigate the causality between the variables over time and across quantiles. Moreover, it
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allows one to test the nonlinear causality of the k-th order across all quantiles of the distribution of the non-oil GDP-macroeconomic variable movements in the current study. As indicated, this study tests not only the non-oil GDP-macroeconomic variable causality in the first order moments (or returns) but also in the second order moments (or variance). The
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nonparametric causality-in-quantile method used in this study is robust to misspecification errors, structural breaks and frequent outliers, which are commonly found in economic time series (Balcilar et al., 2016).
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This paper differs from and adds to the related literature on the investment-growth nexus in five balancing ways. First, we investigate the short-run and long-run relationships
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between non-oil GDP and major related macroeconomic variables including private investments, public investments, oil production and inflation in the largest global oil exporter. Knowing the relevant time horizon is extremely important for decision makers (e.g., speculators, institutional investors, policy makers including annual government budget makers and longer term development planners, etc.), particularly when it comes to choosing the types of government investments and the overall asset allocation for a particular portfolio. Government policy makers embark on annual budgets to deal with short run objectives and
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ACCEPTED MANUSCRIPT multiple year development plans to achieve longer term goals. On the other hand, investors may have multiple investment types in place to account for different financial goals that result in various time horizons. Many macroeconomic variables are economic activity variables, and thus they co-
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move with the business cycle. For Saudi Arabia, the business cycle has two major drivers: oil revenues and government’s stabilization policy and those drivers are related. They in turn move all macroeconomic variables. However, economic policy and actions of decision
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makers affect them differently over time. Moreover, due to heterogeneity of preferences, economic agents may not behave similarly over the different phases of the business cycle
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which can cause asymmetry. Fiscal policy, particularly government spending in Saudi Arabia, will be more responsive to recessions than expansions because of the fear of public dissatisfaction with government policies. The Saudi central bank (SAMA) will be much more willing to change interest rates and regulations during difficult times than during the long run.
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In the short run, there are over- and under- shootings in macroeconomic variables but in the long run the variables converge to the long-run equilibrium, which yields different behaviors and trajectories between the short and long run.
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Interestingly, we choose to focus on a frontier country because Saudi Arabia is a heavily oil dependent economy which has recently acknowledged the potential of the non-oil
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part of its economy and that there is a real danger to the Saudi economy in the future if the world’s demand for oil turns the other corner. Also, the Kingdom is the largest GCC and Middle Eastern economy and accounts for around 47% of the region’s GDP.6 Second, we use the nonlinear ARDL because the relationship between the non-oil GDP and the macroeconomic variables can differ in the short- and long-run as oil prices follow short- and long-term events and stages of the business cycle.
6
http://www.propelconsult.com/non-oil-sector-drives-gcc-gdp-growth/.
6
ACCEPTED MANUSCRIPT Third, we check the validity of the NARDL model through the use of the asymmetric generalized impulse response function and the asymmetric variance decomposition methods. Fourth, we use the nonparametric causality-in-quantile method to examine the relationship between the variables under different economic conditions, which allows the parameter to
RI PT
vary across the quantiles. The linear causality detection techniques heavily rely on conditional means, and therefore fail to capture the conditional tail distribution of the time series. The nonparametric causality-in-quantile approach is robust to misspecification errors, structural
SC
breaks and frequent outliers, which are commonly found in economic time series (Balcilar et al., 2017). Finally, we complete our study by doing a multiplier analysis which permits us to
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determine the contributions of the private and public investments to non-oil GDP in Saudi Arabia.
Our empirical results show strong evidence of a nonlinear relationship between the macroeconomic variables under consideration (i.e., public investments, private investments,
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CPI and oil production) and the non-oil GDP for Saudi Arabia. The NARDL model’s results also reveal that past non-oil GDP shocks affect current non-oil GDP in the short run. Additionally, previous positive public investment shocks increase the non-oil GDP in the
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short-run. However, a negative private investment shock decreases non-oil GDP in the shortrun. More interestingly, negative CPI shocks and negative oil production shocks both
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decrease the non-oil GDP in the short-run, probably due to heightened uncertainty about the sustainability of oil exports and sprouting inflation. On the other hand, positive oil production shocks increase the non-oil GDP in the short term. In the long run, a positive public investment shock significantly increases non-oil GDP as it involves increase in governmental capital expenditures, whereas a negative public investment shock does not impact non-oil GDP probably due to a rising compensating social fiscal spending during difficult times. We find a negative linkage between private investment
7
ACCEPTED MANUSCRIPT and the non-oil GDP in the long-run probably due to the countries’ heavy dependence on oil revenues which make up 80% of the government budget and probably due to the occurrence of some crowding out of public investment. Both negative and positive CPI shocks have a significant positive impact on non-oil GDP, which may have to do with rising aggregate
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demand in an environment of falling and rising prices in the country. Further, a positive shock in oil production significantly increases the non-oil GDP as a good part of this sector is oilrelated, while a negative oil production shock reduces it.
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To deepen the analysis, we use the nonparametric causality-in-quantiles and find that previous public investment Granger causes in-mean and in-variance the non-oil GDP across
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various quantiles, with the exception of the highest and lowest quantiles. In contrast, we find little evidence that private investment Granger causes the non-oil GDP probably due to its subordinate role to public investment. Oil production Granger causes in-the mean and in-the variance the non-oil GDP for almost all quantiles (under different economic conditions). For
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CPI, we find no Granger causality-in-mean and in-variance of the non-oil GDP. In contrast, the non-oil GDP does no Granger-cause the macroeconomic variables, probably due to the dominance of the oil sector in this highly oil-dependent economy.
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The remainder of this study is organized as follows. Section 2 provides a review of the literature. Section 3 discusses the methodology. Section 4 describes the data and presents
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some preliminary analyses. Section 5 discusses the empirical results and draws implications for risk management. Section 6 provides concluding remarks.
2. Literature review In the related literature, many studies taking different strands have examined the linkages between investments and economic growth. The first strand deals with the impact of foreign direct investment (FDI) inflows and portfolio investment on long-run economic growth. The
8
ACCEPTED MANUSCRIPT second examines the impact of public investment on long-run economic growth, while the third strand focuses on the relationship between private investment and economic growth. The first strand examines the impact of FDI on economic growth. Feeny et al. (2014) examine the impact of FDI on the economies of the Pacific region and document that this
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impact is positive but lower in Pacific countries than in host countries on average. The results show that a 10% increase in the ratio of FDI to host gross domestic product (GDP) is associated with an economic growth of about 2% in all countries on average. The impact in
SC
Pacific countries falls to between 0.1 and 0.4%. Feeny et al. (2014) suggest that FDI displaces domestic investment in the region.
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Albulescu (2015) explores the impact of FDI and foreign portfolio investment inflows on long-term economic growth in Central and Eastern European countries. The results demonstrate that both direct and portfolio investments exert a positive influence on the longterm economic growth, when we also consider equity and investment funds’ instruments. The
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author shows that incentive packages should be oriented toward both types of investments. Adam (2009) analyzes the impact of FDI and domestic investment on economic growth in Sub-Saharan Africa for the period 1990–2003. The results show a strong positive nexus
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between domestic investment and economic growth The study also finds that FDI has an initial negative effect on domestic investment and a subsequent positive effect in later periods
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for the panel of the studied countries. The sign and magnitude of the current and lagged FDI coefficients suggest a net crowding out effect. Gui-Diby (2014) examines the impact of FDI inflows on economic growth in 50
African countries over the period 1980–2009 and shows that these inflows have a significant positive impact on economic growth and that the scarcity of qualified human resources does not limit the impact of FDI. More recently, Iamsiraroj (2016) studies the relationship between FDI and economic growth using the simultaneous system of equations approach for 124
9
ACCEPTED MANUSCRIPT cross-countries over the period 1971–2010. The author finds that the overall effects of FDI are positively associated with growth and vice versa. The labor force, trade openness and economic freedom are other key determinants of FDI, which in turn promotes income growth further.
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In the second strand of the literature, Kireyev (1998) explores the linkages between public spending and non-oil GDP growth. This author finds a significant and positive relationship between public spending and economic growth in non-oil GDP (i.e., a 1%
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increase in public expenditure causes about a half percent increase in non-oil GDP). Buffie (1995) investigates the short- and long-run effects of cutting investment in social
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infrastructure in a simple perfect foresight model. The author shows that private investment increases in the short run, provided that the intertemporal elasticity of substitution is not extremely large. Furthermore, the author finds that inflation may be higher throughout the adjustment process if a modest degree of intertemporal substitution is possible.
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Jun (2003) assesses China’s economic growth with respect to the growth of investment at the aggregate level and explores the investment–growth nexus during the period of high growth (i.e., 1978-2000). The author demonstrates that China has since realized its high
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growth, without giving rise to an increasing proportion of investment to GDP and to a rise in the incremental capital-output ratios (ICORs). Moreover, investment efficiency was largely
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reaped through the rural industrialization and proliferation of small firms in the non-state sector.
Qin et al. (2006) examine investment-driven growth in China and show an existence of
a long-run positive relationship between investment and economic growth, but the causality runs from the latter to the former. The authors nullify the view that investment drives output growth. Ghazi and Starr (2011) investigate the relationship between government spending and non-oil GDP in Saudi Arabia. Those authors find that increases in government spending have
10
ACCEPTED MANUSCRIPT a positive and significant long-run effect on the rate of economic growth. In addition, they examine the effects of current expenditure on economic growth and demonstrate that government investment in infrastructure and productive capacity has been less growthenhancing in Saudi Arabia than programs designed to improve the administration and
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operation of government entities. Alshahrani and Alsadiq (2014) investigate the impacts of different types of government expenditures on economic growth in Saudi Arabia, using different methods to investigate the short- and long-run effects of these expenditures on
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economic growth. The results indicate that while private domestic and public investments, as well as healthcare expenditure, stimulate growth in the long run, openness to trade and
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spending in the housing sector can also boost production in the short-run.
Mann and Sephton (2015) examine the effects of private investment and different categories of public expenditure (defense, education, health care and housing) on real non-oil GDP in Saudi Arabia, using unit root stationarity and cointegration analysis with vector-error
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correction models. The authors show that public expenditures on health care and defense have decreased real non-oil GDP, while public expenditure on education and housing have very little impact. Furthermore, public expenditures on health crowd-out private investment. Harb
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(2009) investigates the long run and short-run relationships between oil exports, non-oil GDP and investment in five GCC countries. The author considers the effect of cross-sectional
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correlations and applies the corresponding panel unit root tests to study the long-run characteristics of the data series. The author finds that resource exports have no long-run relationship with the macroeconomic variables. In the short-run dynamics, the VAR analysis shows that the effect of oil exports on those variables depends on local policies. Using the general equilibrium approach to fiscal policy, Nakibullah and Islam (2007) explore the effects of government spending on non-oil GDP of Bahrain. The authors find that the positive multiplier effect of permanent domestic government consumption is substantially
11
ACCEPTED MANUSCRIPT neutralized by the negative impact of temporary US government spending on non-oil GDP of Bahrain. Illegbinosa et al. (2012) examine the impact of macroeconomic variables (i.e., exchange rate, interest rate, government capital expenditures and government recurrent expenditures) on the performance of the Nigerian economy, using the ordinary least square
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(OLS) and cointegration test analysis. The results show that exchange rate, government capital expenditures and government recurrent expenditures are positively related to non-oil exports, the agricultural sector, the manufacturing sub-sector and gross domestic product,
manufacturing sub-sector and gross domestic product.
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while the interest rate is negatively related to non-oil exports, the agricultural sector, the
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Overall, it is apparent from this review of the literature that previous empirical studies that investigated the linkages across investments and economic growth have arrived at mixed conclusions, by using different empirical methods and datasets. Obviously, there is still more room for fresh research, particularly on Saudi Arabia, that uses more modern techniques and
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makes new contributions. This paper attempts to investigate the impact of public and private investments on non-oil GDP, while taking into account the presence of influential macroeconomic and oil variables in this country, using models suitable for nonlinearity-based
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stylized facts such as structural breaks and the presence of outliers.
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3. Empirical methods
This section presents the nonlinear ARDL model and the nonparametric causality-in-quantile model.
3.1. The nonlinear ARDL model The asymmetric bound-testing approach (NARDL hereafter) developed by Shin et al. (2014) is used in the current study to examine the presence of asymmetric effects in the short- and
12
ACCEPTED MANUSCRIPT long-run relationships among non-oil GDP and major macroeconomic variables in Saudi Arabia. The NARDL approach is an asymmetric extension of the linear autoregressive distributed lag (ARDL) procedure proposed by Pesaran et al. (2001). Notably, the dynamic error-correction representation of the NARDL approach allows one to capture asymmetry in
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both the short- and long-run. The model also performs better in small samples, compared to other conventional available counterparts (Romilly et al., 2001). The NARDL is also flexible as it provides valid results regardless of the integration order of the variables, i.e., I(0), I(1) or
SC
a combination of both (Nusair, 2016).
Following Shin et al. (2014), the NARDL model is built around the following
= + +
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asymmetric long-run equilibrium relationship: 7
(1)
where is a stationary zero-mean error process that represents deviations from the long-run equilibrium, and are the associated asymmetric long-run parameters and xt is the vector
= + +
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of regressors decomposed as:
(2)
where is an arbitrary initial value and and denote the partial sum processes which
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accumulate positive and negative changes in , respectively, and are defined as follows: (3)
= ∑ ∆ = ∑ min (∆ , 0)
(4)
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= ∑ ∆ = ∑ max (∆ , 0)
The following asymmetric error correction model can be obtained:
$
∆ = + + + + ∆ + ! ∆ + ! ∆ " + #
(5)
7
See Shin et al. (2014) for a more detailed derivation of the NARDL model.
13
ACCEPTED MANUSCRIPT where all variables are as defined above, = − and = − , and the short-run adjustments to positive and negative changes in the explanatory variables xt are captured by ! and ! , respectively. The empirical implementation of the NARDL method entails the same steps as in the
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linear ARDL model. In the first step, the error-correction model in Eq. (5) is estimated by the standard OLS. The second step involves testing for the presence of an asymmetric long-run relationship among the levels of the variables using the bounds testing approach. This can be
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done using either of the following two statistics (Shin et al., 2014). The first is the F-statistic, introduced by Pesaran et al. (2001) and denoted by &'(( , which tests the null hypothesis of no
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cointegration ( = = = 0) against the alternative of cointegration ( ≠ ≠ ≠ 0). The second one is the t-statistic, proposed by Banerjee et al. (1998) and denoted by *+,- , which is suitable for testing the null hypothesis of no cointegration against the alternative of cointegration. Since these two statistics have non-standard distributions that depend on the
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order of integration of the underlying variables, the pragmatic bounds-testing procedure advanced by Pesaran et al. (2001) is also utilized in the NARDL approach. The third step consists of testing for long-run symmetry ( = ) and short-run
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$ 8 symmetry (∑$ / !.,/ = ∑/ !.,/ ) by means of the standard Wald tests. The preferred model
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is selected by starting with p=4 and q=4 and then dropping all insignificant regressors. The results of bound testing and the Wald tests testing the short- and long-run
asymmetries are reported in Table 4. The results of &'(( and *+,- statistics confirm the longrun relationship between the variables and our final model, based on the findings of the shortrun and long-run Wald tests takes the following form:
8
In line with previous relevant studies employing the NARDL model (e.g., Jammazi et al., 2015; Katrakilidis $ $ and Trachanas, 2012; Nusair, 2016), the less restrictive case of short-run asymmetry, i.e. ∑/ !.,/ = ∑/ !.,/ , is considered in this study.
14
ACCEPTED MANUSCRIPT ∗ ΔGDP∗ = + 567 + 789:;< + 789:;< + = 7>:;< + ? @7: + $
$
∗ A B7 + A B7 + ∑/ / ΔGDP/ + ∑/ !,/ Δ789:;< + ∑/ !,/ Δ789:;< + ∑$/ !=,/ Δ7>:;< + ∑$/ !=,/ Δ7>:;< + ∑$/ !?,/ Δ@7: + ∑$/ !?,/ Δ@7: + $ ∑$/ !A,/ ΔB7 + ∑/ !A,/ ΔB7 + #
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(6)
where 567∗ , 789:;< , 7>:;< , @7: and B7 stands for the non-oil GDP, public and private investment, consumer price index and oil production in period t, respectively and #
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refers to the error term. In turn, 789:;< , 789:;< , 7>:;< , 7>:;< , @7: , @7: , B7 and
variables, respectively.
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B7 are the partial sums of positive and negative changes in each of the explanatory
In the fourth step, the asymmetric cumulative dynamic multiplier effect on of a unit change in and can be derived, respectively, as follows: EFGHI EJGH
, CD = ∑D
EFGHI EJGK
, ℎ = 0,1,2, … …
(7)
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CD = ∑D
Note that as ℎ → ∞ , then CD → and CD → , where and are calculated as = − / and = − /, respectively. As argued by Fousekis et al. (2016), depicting
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and analyzing the paths of adjustment and the duration of the disequilibrium following a positive or a negative shock affecting the system provides useful information on the long-run
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and short-run patterns of asymmetry. We also provide the asymmetric impulse response function and variance
decomposition of our specification as in Equation (7) following Hatemi-J (2011)9.
3.2. Causality in the quantile approach We follow Balcilar et al. (2016) to use the novel nonlinear causality method to examine the causality-in-quantiles between non-oil GDP ( ) and corresponding macroeconomic variables 9
Technical details, not presented here for brevity of space, can be seen in Hatemi-J (2011).
15
ACCEPTED MANUSCRIPT ( ). This hybrid nonparametric quantile causality approach is an extension of the work by Nishiyama et al., (2011) and Jeong et al., (2012). Following Jeong et al., (2012)10, we test that does not cause in the -quantile with
regards to the lag-vector of
UV W , … , , , … , " = UV W , … , " However, presumably
causes
in
the
-th
S , … , , , … , T if
with
(8)
regards
to
(9)
UV ( | ∙) is the -th quantile of . The conditional quantiles of , UV ( | ∙)
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where
quantile
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UV W , … , , , … , " ≠ UV W , … , "
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S , … , , , … , T if
depends on t and the quantiles are restricted between zero and one, i.e., 0 < < 1. Let us define vectors a ≡ ( , … , ) , c ≡ ( , … , ) , and d = (c , a ). The functions &FG|eGKf ( |d ) and &FG|gGKf ( |a ), be the conditional distribution functions of conditioned on vectors d and a , respectively. The conditional
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distribution &FG|eGKf ( |d ) is presumed to be completely continuous in for nearly all d . By defining UV (d ) ≡ UV ( |d ) and UV (a ) ≡ UV ( |a ) we can see that
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&FG|eGKf hUV (d )|d i = , which holds with a probability equal to one. Accordingly, the causality-in-quantiles hypothesis based on Equations (8) and (9) can be
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represented as:
j : 7S&FG|eGKf hUV (a )|d i = T = 1
(10)
j : 7S&FG|eGKf hUV (a )|d i = T < 1
(11)
In order to define a measurable metric for the practical implementation of the causality-in-quantiles tests, Jeong et al., (2012) make use of the distance measure l = h# m(# |d )ne (d )i , where # denotes the regression error and ne (d ) denotes the
10
The exposition in this section closely follows Nishiyama et al. (2011) and Jeong et al. (2012).
16
ACCEPTED MANUSCRIPT marginal density function of d . The estimator of the unknown regression error is expressed as: #̂ = ph ≤ UrV (a )i −
(12)
In Equation (9), the quantile estimator UrV (a ) yields an estimate of the -th
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conditional quantile of given a . We estimate UrV (a ) by employing the nonparametric kernel approach as: ( |a) UrV (a ) = &rF G |gGKf
(13)
uGKf KuvKf ∑z x phFv yFG i v{|Hf,v}G st w
uGKf KuvKf ∑z x v{|Hf,v}G st w
(14)
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&rFG|gGKf ( |a ) =
SC
where &rFG|gGKf ( |a ) denote the Nadarya-Watson kernel estimator given by:
with ~(∙) denotes a known kernel function and ℎ is the bandwidth used in the kernel estimation.
Next, we examine the causality-in-variance (2nd moment) because the rejection of
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causality in the moment C does not imply non-causality in the moment for C < , from the macroeconomic variables to the volatility of the non-oil GDP. We can illustrate this by utilizing the following model:
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= (c , a ) + #
(15)
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where the higher order causality-in-quantiles can be tested as: j : 7 &F |eGKf hUV (a )|d i = = 1 for = 1,2, … ,
(16)
j : 7 &F |eGKf hUV (a )|d i = < 1 for = 1,2, … ,
(17)
G
G
We test that Granger causes in quantile up to -th moment using Equation
(16) to formulate the feasible kernel-based test statistic following Jeong et al. (2012), for each . For the joint density-weighted nonparametric tests for all = 1,2, … , , we follow the sequential testing approach as in Nishiyama et al. (2011). The lag order of 1 is chosen 17
ACCEPTED MANUSCRIPT based on the Schwarz Information Criterion (SIC) in a VAR model. The bandwidth value is selected by using the least squares cross-validation techniques. Finally, for (∙) and ~(∙) we employ Gaussian-type kernels.
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4. Data and preliminary analysis 4.1. Data
We use quarterly-frequency data for public investment (PUBINV), private investment
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(PRINV), consumer price index (CPI) as proxy of inflation, oil production (OP) and non-oil GDP (GDP*). The sample data covers the period ranging from 1992:Q1 to 2014:Q4, totaling
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92 quarterly observations which are widely sufficient for empirical analysis based on the central limit theorem (CLT). The sample data is dictated by their availability and is sourced from different websites: quandl website (https://www.quandl.com/data-sources) and the Central Department of Statistics & Information of Saudi Arabia (http://www.cdsi.gov.sa). The
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choice of oil production is motivated by the fact that rising oil price has been an important factor driving economic growth.
The CPI is the benchmark inflation and plays a key role in affecting the economic
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growth as well as investment. We note that there is no persuasive empirical proof for either a positive or a negative relationship between inflation and economic growth. The early part of
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the literature shows a negative impact of inflation on economic growth (Barro, 1995; Fischer, 1983; Valdovinos, 2003), while the most recent compilation of the literature finds a positive effect between inflation and economic growth (Mallik and Chowdhury, 2001; Rapach, 2003; Benhabib and Spiegel, 2009; Pradhan et al., 2015). All the considered variables are crucial for macroeconomics policies (Easterly and Rebelo 1993; Frenkel and Khan 1990; Bleaney 1996; Cordon, 1990; Blomstrom et al., 1996; Madsen, 2002). For empirical analysis, we transform
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ACCEPTED MANUSCRIPT these variables by taking natural logarithms to correct for potential heteroskedasticity and dimensional differences between the series. Figure 1 plots the trajectory of the considered variables. As shown in this figure, we can observe that non-oil GDP increases along the sample period. More precisely, this variable
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exhibits a significant increase after 2004. The graphical analysis shows that the Saudi government is trying to diversify sectors of the economy to increase the total GDP. Looking at private investment, we can observe that between 1992 and 2005 this economic activity
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variable is relatively stable and exhibits a significant increase after 2008. Between 2008 and 2011, this investment also increases during a period that is marked for the most part by an
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historic increase in the oil price. The CPI exhibits a sharp increase following the 2007/2008 GFC. Oil production decreased in 2003 which corresponds to the 2003 Gulf war and increased in 2008 which corresponds to the dramatic surge in oil prices.
b). Public investment
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a). Non-oil Gross Domestic Product (GDP*)
c). Private investment
d). Consumer price inflation
19
ACCEPTED MANUSCRIPT
RI PT
e). Oil production
Fig. 1. Time-paths of the quarterly macroeconomic variables.
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Note: Non-oil GDP is in million riyals; public and private investments are in current US$; oil production is in million barrels.
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4.2. Preliminary analysis
Table 1 presents the descriptive statistics for non-oil GDP, private investments, public investment, consumer price index (CPI) and oil production. As shown in this table, the quarterly value of public investment is significantly higher than their private investment
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counterpart throughout the period. This anecdotal result indicates that the Kingdom should encourage and promote the private sector and there is ample room for that. The mean value of non-oil GDP is equal to SR 683,896.2 million and ranges between a maximum value of
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SR1,653,776 and a minimum of SR302,903.8. More importantly, all series deviate from the normal distribution as indicated by the Jarque-Bera test with the exception of oil production.
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This stylized fact is a source of nonlinearity in our series. The unconditional correlation results among our variables are reported in Table 2. The non-oil GDP growth is positively correlated with both private and public investments. This result indicates that both variables move in the same direction. In addition, oil production is positively related to non-oil GDP, meaning that increasing oil production improves not only oil-GDP but also non-oil GDP growth.
Table 1: Descriptive statistics 20
ACCEPTED MANUSCRIPT PUBINV 737829.3 393449.5 1958955 245236.3 550005.3 0.858441 2.254328 13.43088 [0.0012] 92
PRINV 100119 31396.92 334369.9 5051.903 111689 0.902811 2.120711 15.46144 [0.0004] 92
CPI 85.23716 78.28883 116.2507 72.11067 13.4081 1.140853 2.746759 20.20286 [0.0000] 92
OP 3114.989 3024.074 3588.523 2552.986 265.4633 0.202538 2.133932 3.504281 [0.1734] 92
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Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability No. of Obs.
GDP* 683896.2 471362.2 1653776 302903.8 400131.9 1.018725 2.726316 16.20006 [0.0003] 92
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Note: Data is quarterly, ranging from 1992 until 2014. Non-oil GDP is in million riyals. Public investment is proxied by gross fixed capital formation (current US$) in millions, private investment is the difference between total investment and public investment. CPI is a gauge of consumer price inflation, and oil production is Saudi Oil Production (in million barrels).
PRINV
CPI
1.0000
0.8998*** (19.562) [0.0000] 0.9623*** (33.552) [0.0000] 0.7611*** (11.130) [0.0000]
PRINV
CPI
OP
1.0000
0.8822*** 1.0000 (17.772) [0.0000] 0.5382*** 0.6597*** (6.0573) (8.3285) [0.0000] [0.0000]
1.0000
EP
OP
PUBINV
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GDP* PUBINV
GDP* 1.0000 0.9926*** (77.457) [0.0000] 0.8736*** (17.027) [0.0000] 0.9743*** (41.022) [0.0000] 0.7437*** (10.553) [0.0000]
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Table 2: Correlation matrix for the variables
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Note: This table presents the unconditional correlation among non-oil GDP, private investment, public investment, oil production and CPI. The values in parenthesis are the standard deviations, while those in brackets are the p-values. *** indicates significance at the 1% level.
5. Empirical results
We first establish the unit root properties of the data using the Narayan and Popp
(2010) unit root test, which indicates two structural breaks at unknown locations in the deterministic components of the series. Notably, the standard unit root tests have a low power when the time series exhibit structural breaks over time. One of the main advantages of the Narayan and Popp (2010) unit root test is that it does not require an a priori knowledge about
21
ACCEPTED MANUSCRIPT the timing of possible structural breaks because the break dates are endogenously determined within the model. Another distinctive feature of this test is that breaks are allowed under both the null and alternative hypotheses. Two different specifications are considered by Narayan and Popp (2010) to test the order of integration of a series. Based on the Monte Carlo
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simulations, Narayan and Popp (2013) have shown that this unit root test has better size and power properties and identifies the breaks more accurately than its main two-break unit root rivals, namely the older Lumsdaine and Papell (1997) and the Lee and Strazicich (2003) tests.
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Since the Narayan and Popp (2010) unit root test has already been widely employed in the economic and financial literature, we do not reproduce the details of this test.11
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Table 3 reports the results of the Narayan and Popp (2010) unit root test with two structural breaks. Specifically, model M1 allows two structural breaks only in the level, while model M2 permits two breaks both in the level and the trend of a time series. The results reveal that the variables have a mixed order of integration where few are stationary in the
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level, while others are stationary in the first difference. The most important finding of the Narayan and Popp (2010) unit root test is that none of the variables is found to be I(2). Furthermore, the presence of structural breaks in the time series data gives an early indication
EP
of a asymmetric behavior of the time series over time, and hence underscores the possibility of asymmetric short- and long-run relationships between the variables. The break dates can be
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linked to global economic and political events. The dates of structural breaks for private investment correspond to the 2003 Gulf war which involved the occupation of Iraq. The break dates for other variables also coincide with some significant historic events like the Asian financial crisis of 1997-1998 and the GFC of 2007-2008. Breaks in the time series data with mixed order of integration require the use of the asymmetric bound-testing approach as proposed by Shin et al. (2014) since this approach 11
For a detailed description of the methodology of the Narayan and Popp unit root test, see for example Narayan and Popp (2010 and 2013).
22
ACCEPTED MANUSCRIPT relaxes the usual assumption used in the cointegration analysis which requires that all variables must be integrated of the same order.
Table 3: Results of the Narayan and Popp (2010) unit root test with two structural breaks
TB2
k
1997Q4 2008Q1 2000Q4 1998Q2 1998Q4
2003Q4 2008Q4 2003Q1 2007Q3 2001Q4
1 5 5 4 5
1996Q4 2008Q1 2000Q4 2007Q2 1998Q4
2003Q1 2008Q4 2003Q1 2008Q4 2001Q4
4 4 4 0 4
Test Statistics
TB1
TB2
k
-0.610 0.588 -4.019* -1.189 -4.641*
1997Q4 2008Q1 2000Q4 2007Q3 1998Q4
2003Q4 2008Q4 2003Q1 2010Q1 2001Q4
1 5 5 0 6
-5.489*** -4.898* -5.140** -5.447** -5.837***
1997Q3 2008Q1 2000Q4 2006Q3 2001Q4
2003Q4 2008Q4 2004Q1 2007Q4 2007Q4
4 4 4 0 4
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Level lnGDP* -3.356 lnPUBINV -0.078 lnPRINV -4.014* lnCPI -4.468** lnOP -3.846 First Difference lnGDP* -4.943** lnPUBINV -5.978*** lnPRINV -4.164* lnCPI -4.318** lnOP -5.497***
TB1
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Test Statistics
Model M2
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Model M1
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Note: This table displays the results of the Narayan-Popp unit root test for models M1 and M2 as explained in Narayan and Popp (2010). Model M1 (M2) assumes two structural breaks at unknown dates in the level (level and slope) of each series. The test statistics for the null hypothesis of a unit root are presented for both the series in the level. The critical values for model M1 are -4.988, -4.316 and -3.980 at the 1%, 5% and 10% significance levels, respectively. The critical values for model M2 are -5.576, -4.937 and -4.596 at the 1%, 5% and 10% significance levels, respectively. These critical values have been collected from Narayan and Popp (2010) based on 50,000 replications for a sample size of 50 observations. TB1 and TB2 are the dates of the structural breaks selected according to the sequential procedure discussed in Narayan and Popp (2010) and k stands for the optimal lag length obtained by using the procedure suggested by Hall (1994) and Narayan and Popp (2010). Following Narayan and Popp (2010), a trimming percentage of 20 is used. As usual, the asterisks ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
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The presence of long-run asymmetric linkages between the Saudi public investment, private investment, oil production, CPI and non-oil GDP is confirmed using the bound-testing procedure. The results summarized in Table 4 also show a nonlinear long-run relationship between the non-oil GDP and the explanatory variables. The long- and short-run asymmetries of each variable are examined using the Wald test. The results indicate that public investment and oil production have a long-run asymmetric impact on non-oil GDP, whereas the impact of private investment and CPI is symmetric in the long-run. This result may be explained by the fact that public investments are based on oil revenues. In addition, the oil market is marked by 23
ACCEPTED MANUSCRIPT strong asymmetry behavior which is due to geopolitics and OPEC decisions (Mensi et al., 2014). As for the short-run, we find strong evidence that all the variables have an asymmetric short-run association with the non-oil GDP.
Statistic 7.3713*** -5.5623*** 6.4230** 37.540*** 2.0760 4.3950** 0.5374 2.9620* 62.610*** 3.6360*
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FPSS *+,WLR(PUBINV) WSR(PUBINV) WLR(PRINV) WSR(PRINV) WLR(CPI) WSR(CPI) WLR(OP) WSR(OP)
p-value [0.0130] [0.0000] [0.1570] [0.0390] [0.4670] [0.0890] [0.0000] [0.0600]
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Tests
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Table 4: Results of the bound tests and the Wald tests for long-run and short-run asymmetry Decision Cointegration Cointegration Asymmetry Asymmetry Symmetry Asymmetry Symmetry Asymmetry Asymmetry Asymmetry
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Note: This table reports the results of the bounds-testing procedure for cointegration in the non-linear ARDL (NARDL) models and the Wald statistics of the long- and short-run symmetry tests for each country. The exact specification and the results of the NARDL models are presented analytically in Table 4. The statistics FPSSNonlinear and tBDM denote the F-statistic and the t-statistic proposed by Pesaran et al. (2001) and Banerjee et al. (1998), respectively, for testing the null of no cointegration in the NARDL model. The critical values for these statistics have been obtained from Pesaran et al. (2001). WLR denotes the Wald statistic for the long-run symmetry, which tests the null hypothesis of = for each explanatory variable in Eq. (7). WSR corresponds to the Wald statistic for the short-run asymmetry, $ $ which tests the null hypothesis that ∑/ !.,/ = ∑/ !.,/ for each explanatory variable in Eq. (7). ***, ** and * indicate a rejection of the null hypothesis of no cointegration and symmetry at the 1%, 5% and 10% levels, respectively.
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Based on the obtained results of the bounds tests and the Wald tests for the long-run and the short-run asymmetry and the confirmation of cointegration among the considered
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variables, it is now legitimate to use the NARDL model. Table 5 reports the dynamic asymmetric estimation of the relationship between the non-oil GDP and the macroeconomic variables. Looking at the short-run results, we find that past non-oil GDP shocks affect current non-oil GDP. In fact, the first lag quarter non-oil GDP significantly increases the next quarter non-oil GDP as the short-run lagged variable is statistically significant. On the other hand, previous positive public investment shocks have a positive impact on non-oil GDP. This result is explained by the fact that the Saudi government invests in the non-oil sector by
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ACCEPTED MANUSCRIPT building large projects such as petrochemicals. Moreover, the non-oil GDP is dominated by the service sectors which benefit from higher income in the economy. These results are not in line with Fasano and Wang (2001) who fail to provide evidence of a causality running from public expenditures to non-oil GDP in the GCC countries.12 Also, negative private investment
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shocks increase non-oil GDP in the short-run. We should note that the oil sector grew by 5.1% year-over-year in the first quarters of 2016, but the non-oil sector shrank by 0.7% which is the weakest reading in at least five years. The non-oil GDP is highly sensitive to
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uncertainties. This weedy statistic should motivate the Saudi government to move forward with its plan to diversify its economy away from the oil sector. More interestingly, CPI and
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oil production shocks have a negative impact on non-oil GDP in the short-run. The latter effect may have to do with the behavior of oil prices or the Dutch Disease. Regarding the long run, a positive public investment shock (L ) significantly increases (0.369) non-oil GDP in the long-run, whereas a negative public investment shock
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(L) does not impact the variable. This result has to do with the underlying characteristic of this huge oil exporter that uses foreign reserves and fiscal policy to stabilize its economy. Private investment does not impact non-oil GDP in the long-run, which may be due to its
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small size relative to that of public investment or it is possible there is a crowding out by
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public investment. On the other hand, CPI has a significant positive (0.798) impact on non-oil GDP which could be a reflection of higher oil prices. Finally, a positive shock in oil production (L) significantly increases (0.399) non-oil GDP because of the connection with the oil sector, while a negative oil production shocks (L) significantly reduces (-0.455) it. It is worth noting that the Saudi government consumption depends on oil production which yields its oil revenues.
12
The included Gulf countries are all GCC countries with the exception of Kuwait.
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ACCEPTED MANUSCRIPT Table 5: Dynamic asymmetric estimations
0.8708 0.369 [0.000] -0.017 [0.140] 0.399 [0.000] 1.483 [0.122] -629.2929
Adjusted R-squared L L± L = BIC
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R-squared L L± L = ( AIC
T-ratio [p-value] 5.57 [0.000] -5.56 [0.000] 4.35 [0.000] 0.30 [0.765] -1.41 [0.164] 4.69 [0.000] 3.21 [0.002] -3.51 [0.001] 2.15 [0.035] 6.13 [0.000] 2.10 [0.039] -1.72 [0.089] -1.91 [0.060]
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Constant ∗ ln GDP ln PUBINV ln PUBINV ln PRINV ln CPI ln OP ln OP Δln GDP ∆ln PUBINV ∆ln PRINV= ∆ln CPI? ∆ln OP
Standard error 0.3213 0.0254 0.0119 0.0187 0.0017 0.0240 0.0175 0.0183 0.0822 0.0366 0.0056 0.1400 0.0408
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∆ln GDP∗ Coefficients 1.7913*** -0.1413*** 0.0522*** 0.0056 -0.0024 0.1127*** 0.0563*** -0.0643*** 0.1767** 0.2248*** 0.0119** -0.2410* -0.0778*
Dependent variable :
0.8386 0.040 [0.765] 0.798 [0.000] -0.455 [0.000] 0.492 [0.415] -517.8127
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Note: This table reports the results of the estimation of the best-suited NARDL model for the adjustment of the non-oil GDP. The superscripts “+” and “-” denote the positive and negative partial sums, respectively. ~J and ~J are the estimated long-run coefficients associated with positive and negative changes of the variable x, respectively, defined by ~r = − r. Adj. R2 represents the value of the adjusted R2 coefficient of the estimated
= model. ( and = denote the LM tests for serial correlation and heteroscedasticity, respectively. The *** ** superscripts , and * indicate the 1%, 5% and 10% significance levels, respectively. The standard errors and the p-values are in parentheses and brackets, respectively.
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Figure 2 plots the evolution of the adjustments of the cumulative dynamic multipliers
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derived from the NARDL model for the non-oil GDP with respect to each explanatory variable (private investment, public investment, oil production and CPI). These multipliers display the patterns in which non-oil GDP adjusts to its new long-term equilibrium following a negative or a positive unitary shock in public investment, private investment, CPI and oil production. The estimated dynamic multipliers are based on the best-fitting NARDL model selected by the Akaike information criterion. The positive (dashed green line) and negative (dashed red line) changes capture the adjustment of the non-oil GDP to positive and negative shocks in the variables under discussion at a given forecast horizon of 40 quarters. The 26
ACCEPTED MANUSCRIPT asymmetric curve (continuous blue line) represents the difference between the dynamic multipliers associated with positive and negative shocks i.e.,
m h+ − m h−
. This curve is displayed
along with its lower and upper bands (blue background) at the 95% confidence interval and presents a measure of the statistical significance of asymmetry at any horizon h. non-oil GDP to a unitary positive change in public
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The adjustment pattern of
investment is positive with a significant asymmetry more towards positive shocks probably because of small crowding out and the government’s active stabilization policy in the face of
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bad shocks. The non-oil GDP also achieves a new equilibrium within approximately one year (i.e., within a four-quarter time period) of the occurrence of a shock in public investment. In
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contrast, the effect of positive and negative private investment shocks on the non-oil GDP is symmetric and negative over long-run; however, it is asymmetric in the short-run. The CPI symmetric (asymmetric) shocks increase non-oil GDP in the long-run (short-run). A positive (negative) shock in oil production asymmetrically increases (decrease) the non-oil GDP in the
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long-run. Overall, the non-oil GDP achieves a new equilibrium point within 10 quarters after a unitary shock to a particular explanatory variable such as public investment.
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Figure 2: Dynamic multipliers from the NARDL model for each variable
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ACCEPTED MANUSCRIPT Cumulative effect of PRINV on GDP*
0
10
20 Time periods
30
40
0
10
20 Time periods
positive change
negative change
positive change
asymmetry
CI for asymmetry
asymmetry
Note: 90% bootstrap CI is based on 100 replications
30
40
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0
.1
-.02 -.01
.2
0
.3 .4
.5
.01 .02
Cumulative effect of PUBINV on GDP*
negative change
CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of OP on GDP*
-1
-.5
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-.5
0
0
.5
.5
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1
1
Cumulative effect of CPI on GDP*
0
10
20 Time periods
30
40
0
10
20 Time periods
30
40
positive change
negative change
positive change
negative change
asymmetry
CI for asymmetry
asymmetry
CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Note: 90% bootstrap CI is based on 100 replications
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Notes: These figures display the dynamic multipliers showing the pattern of adjustment of the non-oil GDP (GDP*) to the new long-run equilibrium following a negative or positive unitary shock in each of the explanatory variables. The dynamic multipliers have been estimated based on the best-suited NARDL model reported in Table 5. The vertical axis shows the asymmetric coefficients, while the horizontal axis indicates the time periods in quarters.
Next, we present the asymmetric generalized impulse response (AGIR) functions and
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asymmetric variance decompositions (AVD), following Hatemi-J (2011) with the same specifications as in Equation (7). The impulse response functions are used to determine the propagation process of a shock over time (Sims, 1980). A VAR model that treats all variables in the model endogenously is used.13 Figure 3 demonstrates the response of the non-oil GDP (GDP*) to the variables represented in the cumulative positive/negative changes. It can be clearly seen that the response of non-oil GDP to the explanatory variables is in line with our 13
To honor space limitations, we do not provide the technical details of the asymmetric impulse response functions and asymmetric variance decompositions. These details can be found in Hatemi-J (2011); however, the positive and negative cumulative sums of the variables are the same as used for the NARDL estimation.
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ACCEPTED MANUSCRIPT findings of the NARDL model, thus the AGIR can be considered as a robustness test to the previous dynamic multipliers of the NARDL model.
Response of GDP* to PUBINV (+)
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Figure 3: NARDL impulse response to generalize one standard deviation innovation ± standard error Response of GDP* to PRINV
Response of GDP* to PUBINV (-) .008
.008
.006
.006
.006
.004
.004
.004
.002
.002
.002
.000
.000
.000
-.002
-.002
-.002
-.004
-.004 2
3
4
5
6
7
8
9
-.004
1
10
2
3
.008
.008
.006
.006
.004
.004
.002
.002
.000
.000
-.002
-.002
3
4
5
6
7
8
6
7
8
9
10
1
2
9
10
3
4
5
6
7
8
9
10
Response of GDP* to OP (-)
.008 .006 .004 .002 .000
-.002 -.004
-.004
-.004 2
5
Response of GDP* to OP (+)
Response of GDP* to CPI
1
4
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1
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.008
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
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Notes: These figures show the asymmetric generalized impulse response functions of the non-oil GDP (GDP*) for each of the explanatory variables. These GIRFs are obtained through VAR using the specifications of the best fitted model reported in Table 5. The vertical axis is expressed in units of non-oil GDP (GDP*). The solid blue line is a point estimate for the quantity GDP* expected to change following a unit impulse over the number of periods (in quarters) on the horizontal axis.
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Table 6 reports the results of the asymmetric variance decompositions (AVD) for
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non-oil GDP (GDP*) with respect to the four explanatory variables. The non-oil GDP explains 79.37% of the variation in its own forecast errors over a 16-quarter time horizon. The positive and negative shocks of the public investment explain around 3.89% and 2.03% of the variation in the forecast error of non-oil GDP over the same time horizon. The CPI explains 4.63% of the variation in the forecast error of the non-oil GDP, whereas the negative oil production shocks explain around 8.70% of these variations in the non-oil GDP over the 16 quarters. These findings are in line with the estimation results of the NARDL model.
29
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ACCEPTED MANUSCRIPT However, we test the validity of the NARDL model through the application of AGIR and AVD.
Table 6: Asymmetric Variance Decomposition of GDP* GDP* 100.000 97.633 96.576 95.611 90.606 84.888 79.368
PUBINV+ 0.000 0.584 0.653 0.638 1.103 2.410 3.895
PUBINV0.000 1.597 2.067 2.240 2.275 2.121 2.027
PRINV 0.000 0.052 0.202 0.397 0.856 0.803 0.995
CPI 0.000 0.053 0.220 0.495 2.131 3.625 4.632
OP+ 0.000 0.017 0.066 0.137 0.378 0.422 0.386
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S.E. 0.006 0.006 0.007 0.007 0.007 0.007 0.008
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Period 1 2 3 4 8 12 16
OP0.000 0.064 0.216 0.482 2.651 5.732 8.698
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Note: This table presents the variance decomposition of non-oil GDP (GDP*).
We close the empirical result section by using a novel technique namely the causalityin-quantiles to examine the causality-in-mean (first order) and the causality-in-variance (second order) between non-oil GDP and the four explanatory variables. Since, this method requires stationary time series, we first-difference the time series for this application. It is
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worth noting that our variables exhibit structural breaks and show an asymmetric relationship, which justifies the recourse to the nonparametric causality-in-quantile approach.
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The results are plotted in Figure 4. Looking at the causality from the explanatory variables to non-oil GDP, we observe that public investment Granger causes in-mean and in-
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variance non-oil GDP across the quantiles with the exception of the highest and lowest quantiles. The null hypothesis of no Granger causality-in-mean from public investment to non-oil GDP is rejected at the usual levels of significance over the entire conditional distribution of the non-oil GDP returns, suggesting a predictability for the public investment. More specifically, public investment Granger causes in mean (in variance) the non-oil GDP for the quantiles ranging from q=0.15 to q=0.85 (0.1 to 0.9), suggesting little crowding out if any by public investment. Additionally, we find little evidence that private investment
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ACCEPTED MANUSCRIPT Granger causes non-oil GDP, probably due to its small share in an oil economy that depends heavily on government spending. Concerning oil production, this variable Granger-causes in-mean and in-variance non-oil GDP for the majority of the quantiles. However, we accept the null of no Granger
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causality-in-mean and in-variance for the CPI-non-oil GDP pair. By looking at the trajectories for the causality-in-quantiles from non-oil GDP to the other macroeconomic variables, we accept the null hypothesis of no Granger-causality for all pairs except for the oil production
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oil production for the quantiles q=0.35 and q=0.4.
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variable. More precisely, we find a significant Granger causality-in-mean and in-variance for
Figure 4: Non-parametric causality-in-mean and variance at various quantiles GDP* -/-> PUBINV
GDP* -/-> PRINV
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PRINV -/-> GDP*
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PUBINV -/-> GDP*
CPI -/-> GDP*
GDP* -/-> CPI
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GDP* -/-> OP
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OP -/-> GDP*
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Note: The figure plots the estimates of the nonparametric causality tests of the various quantiles. The y-axis reports the test statistics, while the quantiles are placed on the x-axis. The horizontal black solid lines represent the 5% critical values (CV).
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6. Conclusions and policy implications
Investment is one of the most important and fundamental sources of economic growth. More explicitly, both public and private investments usually play a vital role in increasing
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capacity and production of goods and services in all economies and they can be complementary to each other. Over the recent years, the successive financial crises including
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the global financial crisis of 2008-2009 and the European sovereign-debt crisis of 2010-2011 have curtailed investment and economic growth and incapacitated fiscal policy as a tool of providing public investment. The recent plunges in oil prices have strained the resources of oil-exporting countries and hindered their ability to enhance public investment, expand output capacity and foster economic growth. This study analyzes the effects of private investments, public investments and other macroeconomic and oil variables on
non-oil GDP of Saudi Arabia which has several non-
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ACCEPTED MANUSCRIPT standard investment and inflation characteristics, and thus stands to provide unusual and interesting results than is the case in well-diversified developed countries. To demonstrate the economic relevance of our empirical findings, this study adopts two methods. First, it applies the asymmetric nonlinear autoregressive distributed-lag
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(NARDL) model, with positive and negative partial sum decompositions of the macroeconomic and oil variables, to the Saudi non-oil GDP and inflation to harness the longrun information contained in the levels of the variables under consideration and to avoid
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having a spurious regression at the same time. This model has advantages over the conventional approach of Johansen and Juselius (1990), including its practical suitability
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irrespective of whether the underlying regressors are I(0), I(1) or mutually cointegrated. We also derive the impulse response function for non-oil GDP using this model. Second, we apply the nonparametric causality-in-quantile method to assess the relationship between the variables across the quantiles. More precisely, we investigate this relationship using both the
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nonparametric causality-in-mean and the nonparametric causality-in-variance to analyze the impact of public and private investments, oil production and inflation on non-oil GDP. Several major conclusions may be drawn from this analysis. Using the Narayan and
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Popp (2010) unit root test, all macroeconomic variables exhibit significant structural breaks, which coincide with important events, such the recent global financial crisis, and geopolitical
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factors. Furthermore, we find evidence of a significant nonlinear relationship between nonoil GDP and the other macroeconomic variables (public investment, private investment, CPI and oil production), highlighting that the relationship between these variables may differ in the short-and long-run, which has relevance for policy makers in the short run and development planners in the longer run. In the short-run, by using the NARDL model, we find that past non-oil GDP shocks considerably affect current non-oil GDP, underscoring the dynamic steadiness of non-oil
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ACCEPTED MANUSCRIPT economic growth. Further, previous positive public investment shocks increase the non-oil GDP, suggesting very little crowding out if any, which comes in contrast to the traditional investment theory for developed countries. Interestingly, negative private investment shocks increase
non-oil GDP in the short-run, underlying the importance of public investment
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compared to private investment and the compensating role the Saudi government takes to stabilize the economy. It may also imply that the private sector increases its production efficiency to counter adverse shocks. More interestingly, the negative CPI shocks and
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negative (positive) oil production shocks have a negative (positive) impact on non-oil GDP in the short-run, which could be due to heightened uncertainty in the economy caused by
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those shocks. The oil-induced Dutch Disease may also be alive in Saudi Arabia in the shortrun.
In the long run, a positive public investment shock significantly increases the non-oil GDP, again suggesting little crowding out in the long run, whereas a negative public
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investment shock does not impact much the non-oil GDP, probably due to the government’s stabilization policy supported by a huge sovereign wealth fund which is used to fend off political pressure. On the other hand, private investment does not impact non-oil GDP in the
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long-run, again due to its small contribution to this heavily oil-based economy, while both negative and positive CPI shocks have a significant positive impact in the long run because of
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the importance of imports of goods and services in the short-run. A positive shock in oil production significantly increases non-oil GDP of the world’s largest oil exporter in the long run, while a negative oil production shock reduces it. This has policy implications pertinent to how the Saudi government governs its oil policy in OPEC. Using the nonparametric causality-in-quantile framework, we find that past public investment Granger causes in-mean and in-variance non-oil GDP across the quantiles, with the exception of the highest and lowest quantiles, confirming the corresponding “crowding
34
ACCEPTED MANUSCRIPT in” results of the NARDL approach. Moreover, we find little evidence that private investment Granger causes non-oil GDP, emphasizing the small importance of this economic activity in the Saudi Economy, which is also consistent with the corresponding results of the NARDL model. Oil production Granger-causes in mean and in variance non-oil GDP for almost all
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quantiles. For CPI, we find no Granger causality in mean and in variance of the non-oil GDP. In contrast, non-oil GDP does no Granger-cause the other macroeconomic variables as is the case with the NARDL results, underlying the importance of oil exports proceeds in propelling
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the Saudi economy.
These results have several important implications for policymakers and institutional
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investors dealing with the Saudi economy. The investment horizon is a key factor for determining the controversial relationship between inflation and non-oil GDP. In fact, inflation changes from a negative relationship in the short term to a positive relationship in the long term, which confirms the nonlinearity in this nexus. The threshold time is a
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determinant variable for policy makers to establish their short- and long-term strategies to deal with increasing inflation, which is important for the Saudi planning initiative, Vision 2030. Low inflation rates should minimize uncertainties in the financial markets, which in
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turn boosts investment. We should note that the Saudi inflation rate reached 34% and 8% in 1973 and 2008, respectively, and those two years coincide with strong oil booms. In fact, we
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can argue that not only high oil prices and production affect inflation but also monetary expansion which brings in imported inflation due to the effective and actual pegged currency policies, growth in domestic demand and increases in food prices and rental prices which can be considered among the main drivers of inflation in the GCC. Oil production is crucial for oil revenues for Saudi Arabia. In fact, a great part of the total GDP comes from the oil GDP. The negative sign of private investment may be an indication of a crowding out effect on economic growth or a sign of the Dutch Disease in
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ACCEPTED MANUSCRIPT Saudi non-oil GDP in the long run as a result of resource movements. There is also evidence to support the theory of a long-run "crowding-out" effect of public investment. Along this line, we can dare to underscore the role of public investment and oil production to improve the non-oil GDP. The Saudi government remains an oil-dependent economy despite the
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previous diversification efforts to diversify. Its Vision 2030 aiming to diversify away from oil will face major challenges.
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Acknowledgments
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The first author (W. Mensi) greatly appreciates the financial support provided by Scheikh AlFawzan Research Chair for Expectations of Saudi Macro-economy at Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
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Highlights
• We study the impact of the macroeconomic variables on non-oil GDP in Saudi Arabia.
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• We use the nonlinear ARDL and the non-parametric causality-in-quantile methods. • A nonlinear relationship between the macroeconomic variables and the non-oil GDP is found.
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• There is evidence of causality-in-mean and -in-variance emanating for macroeconomic variables.
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• These relationships depend on the time horizons (short- and long-run) and the quantiles.
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