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Evaluating the Effects of Monetary Policy Shocks on GCC Countries
Economic Analysis & Policy, Vol. 43 No. 2, september 2013
Evaluating the Effects of Monetary Policy Shocks on GCC Countries Sayyed Mahdi Ziaei Department of Business Administration Faculty of Management Universiti Teknologi Malayisa (UTM), Skudai Malaysia Email: [email protected] Abstract:
For the first time, this research assesses monetary policy shock effects on GCC members over the last 17 years using a structural vector autoregressive (SVAR) model baseline. While GCC states peg their currency to the US dollar, the contemporaneous coefficient in the structural model indicates that for GCC countries a monetary policy instrument responds positively to unexpected increases in M2, while a monetary aggregate reacts negatively to interest rate shocks. However, our findings indicate that these countries’ interest rate channel is weak. Furthermore, oil price innovation contributes to most output fluctuations in the short horizon, and M2 and Federal Fund Rates shocks are responsible for most output movements in the long horizon.
I. Introduction The objective of this paper is to determine how the Persian Gulf Cooperation Council (hereafter referred to as GCC) economies, namely Bahrain, Kuwait, Oman, Qatar and Saudi Arabia respond to monetary policy shocks. One of the key monetary policy strategies in GCC countries is price stability, but since 2002, inflation in these countries has been increasing. The GCC countries’ oil price boom by 2002 was accompanied by inflation pressure. In fact, inflation decreases the purchasing power of consumers. Meanwhile, from 2001 the dollar has been losing value against major currencies. Depreciating dollar value induces import cost increases for the countries whose imports are mostly from the EU, Japan and China (Sturm et al., 2008), meaning that GCC’s pegged currencies are accompanied by lower purchasing power. Furthermore, the new millennium has witnessed GCC monetary authorities face complicated situations in using interest rate to control inflation. The dropping US interest rate has imposed additional inflationary pressure in GCC countries. However, in order to maintain their parity with US interest rate and control capital inflow GCC countries’ monetary authorities have brought down their interest rates. Furthermore, inflation in GCC countries has decreased the real interest rate in these states in comparison to the higher interest rate levels in the mid-1990s with lower inflation levels. 195
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From a theoretical point of view, because GCC exchange rates are fixed to the US dollar, it seems that GCC monetary policies and interest rates should follow US Federal Reserve policies; but divergent business cycles between the US and GCC (which have slowed down during the US and GCC economic boom before the 2009 worldwide recession1) have raised questions regarding the roles of monetary policies and foreign shocks in aggregate demand of GCC countries. Furthermore, as Guiso et al. (1999) emphasized there are at least three key conditions that must be achieved for a common monetary policy (like a GCC monetary union) to succeed among union members. First, members must accept the final targets of a common monetary policy. Second, the monetary policy transmission mechanism (MTM) effects should be identical among monetary union members. Differences in MTM impact may potentially hinder the assessment of appropriate monetary policy decisions. Finally, if member countries’ inflation and business cycles are similar, chances of implementing common monetary policies increase. In this paper, structural VARs (SVARs) models are employed to evaluate the effects of monetary policy shocks or interest rate changes on output and inflation of GCC countries. The remainder of this paper is organized as follows. In the next section, the literature on GCC monetary policy is reviewed. In Section III we present the econometric SVAR model and a baseline SVAR model that describes how the GCC monetary policy is constructed. In section IV empirical results achieved by SVAR estimation are analyzed. In the last section, results are summarized and various implications are presented.
II. LITERATURE REVIEW There are several theories that analyze the effects of monetary policy on economy. The standard, neoclassical models consider money neutral, implying that a tightening or expansionary monetary policy together with a decrease or increase in money supply would only affect nominal variables but not real variables. In contrast, according to the Keynesian IS/LM model, (money view) an expansionary monetary policy would induce a fall in short-term interest rate. Since the long-term interest rate is the average of the expected future short-term interest rate, by decreasing short-term interest rate, long-term interest rate drops. A declining interest rate causes various types of investment including real estate, business and inventory investment to increase, and also boosts consumption expenditures. Rising investment and consumption result in mounting aggregate demand. There has been relatively little research done on the monetary policy of GCC states. For example, Darrat (1985) evaluated the impact of money on price level fluctuation in Libya, Nigeria and Saudi Arabia. Findings indicate that lower, real income growths and higher money supplies are linked with higher inflation in the three countries. Bennaceur et al. (2006) analyzed the effects of monetary policy transmission on the stock market in nine MENA countries, inclusive of Bahrain, Oman and Saudi Arabia. It was figured out that monetary tightening seems to have a significant impact on stock market return in the case of Bahrain, 1
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GCC economies after 7 years of oil price boom encounter oil price decline and worldwide recession in mid of 2008 and 2009. This worldwide recession accompanies with restrictive monetary and fiscal policy (monetary union treaty commitment) accelerate the slowdown of these countries’ economies.
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Egypt, and Saudi Arabia. Al-Raisi et al. (2007) assessed the significance of monetary policy independence with regards to Oman. Their VAR analysis suggests that inflation responds to monetary variables like interest rate and money growth but the effect is not permanent. The main reason behind the weakness of monetary transmission in Oman is the pegging of the exchange rate to the US dollar. Hassan and Nakibullah (2008) tried to present evidence on whether the capital markets amongst GCC countries and the US are completely integrated. Evidence overwhelmingly indicates that capital is not perfectly mobile between these countries, implying that the monetary authorities of the GCC countries have had some room to the stage management of their monetary policy.
III. SVAR MODEL In this research, a baseline SVAR model with contemporaneous restrictions was selected in order to analyze the effects of monetary policy and foreign shocks on the aggregate demand of GCC states. For VAR analysis, the following structural equation is first assumed:
A0Yt = At X t + Bε t
(1)
Yt = A∗ Xt + ut
(2)
where Yt is the (n × 1) vector of endogenous variables, A0 is a (n × n) matrix of coefficients of simultaneous relations on the endogenous variables, Xt includes the lag of endogenous variables, A is the matrix of coefficients on the lagged variables in the model, εt as a (n × 1) vector of structural innovation is orthogonal, and ∑ = E (ε tε t' ) presents the varianceεt covariance matrix of the structural innovation. Furthermore, εt is orthogonal and has normal distribution, meaning that shocks are uncorrelated and the variance-covariance matrix has a normal distribution with zero mean. The main difficulty with evaluating the structural model is that the real values of A0 and A cannot be directly estimated. Data sampling information is not adequate for identifying additional restrictions. Gottschalk (2001) believed there are too many sets of unlike values of A0 and A, all of which indicate a similar probability distribution of data. This matter estimates the real value for which A0 and is impossible. To resolve this issue, we should obtain a reduced form of model (1), which would explain each endogenous variable exclusively as a function of predetermined variables: With A∗ = A0−1 A and ut = A0−1 Bε t In this situation, in order to recover the structural parameters from the reduced form model or to exactly identify the model as Hamilton (1994) mentioned, the order condition should be satisfied. It means the number of parameters in the covariance matrix of the reduced form should be the same. The variance-covariance matrix of the reduced form is given by:
∑ = E (u u ) or ∑ = (A )∑ u
t
'
u
−1 0
εt
(A0−1 )'
(3)
To achieve identification, it is necessary for the parameters in B and A0 to be recoverable from the reduced form. In Eq. (3), ∑ contains K(K +1) ⁄ 2 parameters, and there are K(K+1) 197
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free parameters in the right side of Eq. (3), so we should impose the 2K2 – K – K(K + 1) ⁄ 2 restriction on B and A0. Because normally the K(K –1) restriction in imposed on B to be diagonal, identification is achieved if at least the K(K – 1) ⁄ 2restriction is imposed on A0. In the VAR modeling with Cholesky decomposition, A0 is considered triangular. However, in structural VAR, A0 can be any structure until it has sufficient restrictions. 3.1. The GCC Countries’ SVAR Models In this research, a non-recursive SVAR framework was applied to analyze the monetary transmission mechanism in GCC states. Structural shocks in SVAR can be identified by inserting some restrictions in the baseline model. The SVAR basic model of the GCC countries’ economies consists of seven variables represented by the following vector, Xt : Xt = (OPWt , FFRt ,GDPt ,CPI t , M t , Rt , Et )
(4)
where OPWt is the world oil price in US dollars, FFRt is the US federal funds rate, GDPt is the gross domestic production, CPIt is the consumer price index, Mt is the monetary aggregate (M2), Rt is the monetary policy instrument, and Et is the nominal effective exchange rate2. In identifying the structural VAR, the Amisano and Gianini (1997) strategy (AB method) was utilized. In this method enough restrictions are imposed on both matrices A0 and B. For the system to be justly identified, it requires 2n 2 − n(n +1) / 2 or 70 restrictions on both A0 and B. Since 42 restrictions are imposed on B (assumed to be a diagonal matrix in the model), another 28 restrictions on A0 are required for the system to be justly identified. The restrictions placed on the contemporaneous relationships among the variables are shown in Equation (5). In the left side of the baseline SVAR model, coefficients bij point out that variable j immediately affects variable i. The identified system A0ut = Bε t is as follows: ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢⎣
1 a21
a31 0
0
0
0 1
0 0
0
a43
a62
0
0 0
a71 a72
1
0 0
0 0
1
0
0
a65
0
a53
a54
a73
a74
0
0 0 0
0
1
a56
a75
a76
1
0 0 0
0
0
a67 1
⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎦ ⎢ ⎣
⎤ utOPW ⎥ ⎡ 1 utFFR ⎥ ⎢ ⎥ ⎢ 0 utGDP ⎥ ⎢ 0 ⎥ = utCPI ⎥ ⎢⎢ 0 utM ⎥ ⎢ 0 ⎥ ⎢ 0 utR ⎥ ⎢ 0 ⎥ ⎣ utE ⎥⎦
0 1 0 0 0 0 0
0 0 1 0 0 0 0
0 0 0 1 0 0 0
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1
⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎦ ⎢ ⎢⎣
ε tOPW ⎤⎥ ε tFFR ⎥ ⎥ ε tGDP ⎥ ⎥ ε tCPI ⎥ ε tM ⎥ ⎥ ε tR ⎥ ⎥ ε tE ⎥⎦
(5)
The first two variables are US interest rate and world oil price, representing the exogenous external shocks. Domestic variable shocks have no effect on these two variables contemporaneously. However, it is expected that the Federal Funds Rate will immediately react positively to the increase in oil price, because US monetary authorities use a tightening policy when they encounter oil price shocks. The interest rate, exchange rate and money aggregate seem to affect the level of output with lag delay. The price level responds instantly to the output 2
198
Except in the case of Kuwait, that nominal exchange rate is used.
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for all GCC countries, nominal effective exchange rate is only for Saudi Arabia and exchange rate is for Kuwait. The reason for this is that the exchange rate is one of the main indicators of price stability adjustment in the instance of Saudi Arabia and Kuwait. Furthermore, as the government determines oil price to be below the international level, domestic price is not supposed to simultaneously react to the oil price. As GCC countries form the main exporter bloc of oil and petrol, the dollar plays a crucial role in these countries’ economies. GDP is projected to respond positively to world oil prices. The fifth variable represents the demand for a money relation, where the demand for money is assumed to react concurrently to the short-term interest rate, price level and output. The sixth variable represents the money supply. The monetary authority sets the interest rate after contemplating the current oil price values (only for Saudi Arabia and Kuwait), money, exchange rate, and more importantly the interest rate of anchor countries as well as the lagged values of all variables in Xt. The exchange rate is included in the equation because of its pass-through effect. Money and exchange rate are entered in the interest rate function contemporaneously because these variables’ data is available instantaneously. Nevertheless, the data of important variables like output and price level is not readily available to monetary authorities, thus such variables are not included in the reaction function of monetary authority. It is anticipated that interest will respond positively to nominal effective exchange rate and money aggregate. Finally, because exchange rate plays the role of a forward-looking asset price (Kim & Roubini 2000), it is assumed that this variable reacts simultaneously to all other variables and describes financial market equilibrium. 3.2. Data and Choice of Variables We examined the relationship between the monetary policy variable and both output and price in five GCC countries, namely Bahrain, Kuwait, Oman, Qatar and Saudi Arabia, using a SVAR method. Quarterly data from 1992Q4 to 2009Q4 was employed in this study. OPW is the world oil price in terms of US dollars, and FFR is the US federal fund rate. Variables OPW and FFR are determined exogenously relative to policy shock. They serve as instruments to isolate exogenous monetary policy shocks. With respect to interest rate the money market rate (MMR) for Kuwait, bank lending rate (LR) for Bahrain, Oman and Qatar, and three month treasury bill (TB) for Saudi Arabia are used as the key, short-term interest rate employed by these countries’ central banks to signal their monetary policy stance. Nominal effective exchange rate (NEER) is used to examine the influences of exchange rate changes on output and price. We applied NEER as opposed to GCC exchange rate (except in the case of Kuwait) because the exchange rate of GCC countries is pegged to the USD over the respective time period. In this way, while the exchange rate is not included in the model (except for Kuwait), the exchange rate fluctuations that may have inflationary effects are captured by NEER. M2 serves as a proxy of monetary aggregate. Output is measured as real GDP due to the lack of quarterly data on the GCC GDP, the Proportional Denton Method is used to extract quarterly GDP and the consumer price index (CPI) is taken as the measure of the general price level. All data is expressed in natural logs except for the interest rates which are shown in levels. Oil price boom effects (rising oil price began in 2002) are considered by the oil dummy in each country’s estimation process. 199
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IV. SVAR EMPIRICAL RESULTS In this section the contemporaneous coefficients in the structural model are estimated, after which the impulse responses and variance decompositions to a domestic tightening monetary shock are presented. In the final part the effects of monetary policy shocks on domestic variables are discussed. 4.1 Lag Length Although different information criteria can be used to specify lag length, some experts like Cheung and Lai (1993) believe that because of the shifting average error term, the choice of lag length with applying information criteria may not achieve suitable results (Ibrahim 2006). Thus, as Hall (1989) and Johansen (1992) proposed, it is possible to determine lag length by testing whether residual is serially uncorrelated in different lag lengths or not. This method is applied for determining lag length in this paper. The model for Kuwait is estimated using 3 lags of quarterly data, while 2 lags are included for Bahrain, Oman and Saudi Arabia and Qatar. 4.2 Contemporaneous Coefficients in GCC Countries’ Structural Models Table 1 presents the contemporaneous coefficients and standard error in the structural model for each GCC country. The main results of the contemporaneous coefficients indicate the following. In the cases of Saudi Arabia, Kuwait and Oman, the contemporaneous effects of monetary aggregate on interest rate are negative. In other words, the interest rate of these countries responds positively to unexpected increases in M2. Moreover, for Saudi Arabia, Kuwait and Oman, the monetary aggregate decreases at the same time as an unexpected increase in interest rate. Regarding Kuwait and Saudi Arabia the contemporaneous effects of oil price on interest rate are negative. This demonstrates that with an increase in oil price, interest rate in these countries decreases instantly. In addition, the contemporaneous effect of oil price on aggregate demand is negative, implying that unexpected changes in oil price cause GDP in all GCC countries to increase. For all GCC countries the simultaneous effect of federal fund rate on interest rate is negative, which emphasizes that these countries’ interest rates have a positive reaction to federal fund rates. 4.3 Interest Rate Channel and Exogenous Shocks for Bahrain In this section, the effects of the monetary policy instrument on aggregate demand and variables of the financial market in the case of Bahrain are assessed. Figure 1 shows the impulse response functions for output and price along with 95% confidence intervals for Bahrain. Positive innovations in LR (lending rate) following a monetary contraction cause the output in Bahrain to fall. The output bottoms out after 7 quarters by 0.010 percent. Also, the price falls instantly, but after one quarter it starts to rise. Table 3 illustrates the variance decompositions for all variables in the baseline model at forecast horizons of 2 through 16 quarters, which explains the percentage fluctuations in a 200
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given variable caused by different shocks. As reported in Table 3, a shock to WOP accounts for 6 percent fluctuation of GDP after 2 quarters and 25 percent after 16 quarters. LR contributes 10 and 7 percent of movement in output, and 5 and 3 percent fluctuation of price, respectively after 8 and 16 quarters. Moreover, 13 fluctuations of output are related to FFR and 9 percent movement of output is due to M2 after 16 quarters. In addition, 54 and 44 percent fluctuation of NEER and 27 percent movement of M2 after 2 and 4 quarters, respectively, are due to LR shock. Interestingly, shocks in all variables contribute to LR fluctuation. Oil price, FFR, GDP, CPI and M2 account for 28, 17, 16, 14 and 22 percent fluctuations in LR after 16 quarters, respectively. Furthermore, NEER accounts for 12 and 6 percent movement of LR after 2 and 4 quarters, respectively. 4.4 Interest Rate Channel and Exogenous Shocks in the Case of Kuwait Figure 2 illustrates the impulse response functions for Kuwait’s output, price, M2 and ER (nominal exchange rate) following an increase in money market rate. Positive innovation in MMR (money market rate) by approximately 44 basis points following monetary contraction causes output in Kuwait to fall after 1 quarter. Output declines by 0.016 percent after 16 quarters, meanwhile price level responds immediately to MMR shock. Price falls in 1 quarter and rises after that. Table 4 clarifies variance decomposition for each variable. As reported in this table, a shock to WOP accounts for 29 and 17 percent fluctuation of GDP after 8 and 16 quarters, respectively. Meanwhile, 23 and 25 percent fluctuation of GDP is related to FFR and monetary aggregate shocks respectively, after 16 quarters. So this means that the significant fluctuation in output is related to monetary aggregate and foreign shocks in the long term. MMR contributes to 7 percent movement in output level after 16 quarters and 10 percent fluctuation in price level after 2 and 4 quarters. This result indicates that interest rate innovations are weak determinants of output movements. While interest rate shock contributes to price movement effectively in the short term, GDP shock accounts for 36 percent fluctuation of price level after 16 quarters. Furthermore, the MMR accounts for 14 and 11 percent ER movements after 2 and 4 quarters, respectively. Results also show that FFR accounts for 53 and 43 percent fluctuations of MMR after 8 and 16 quarters respectively. In addition, 17 and 11 percent fluctuations of MMR after 2 and 4 quarters respectively, are attributed to exchange rate shocks. 4.5 Interest Rate Channel and Exogenous Shocks in the Case of Oman The impulse response function of output, price, nominal effective exchange rate and monetary aggregate following a monetary tightening is provided in Figure 3. A positive innovation in LR by approximately 10 basis points following a tightening of monetary policy induces an output and price drop in Oman. After 16 quarters, output falls 0.02 percent and price bottoms out at 12 quarters after the shock. Table 5 demonstrates variance decomposition for each variable. It is evident that a shock to WOP accounts for 15 and 12 percent of GDP fluctuations after 2 and 4 quarters respectively, while 8 and 7 percent of GDP fluctuation relates to NEER after 8 and 16 quarters respectively. LR contributes to 9 and 8 percent of GDP fluctuation and CPI respectively, after 16 quarters. 201
Evaluating the Effects of Monetary Policy Shocks on GCC Countries
These outcomes show that interest rate innovation is a weak element of movement in economic activity. In spite of the fact that interest rate shock contributes to price movement in the long run, the main fluctuations of price level are due to output shock. 28 percent fluctuation of price after 16 quarters is related to output shock. Furthermore, LR accounts for 33 and 44 percent movement of monetary aggregate and 32 and 37 percent fluctuation of NEER after 4 and 8 quarters, respectively. Moreover, a shock in all variables contributes the main effect on interest rate. NEER, M2 and past shocks of LR account for the main effects on LR in the short term and GDP, CPI and FFR account for the main effect on LR in the long run. NEER contributes 18 and 15 percent while M2 contributes 27 and 24 percent movement of LR after 2 and 4 quarters, respectively. Moreover, FFR, GDP and CPI account for 31, 13 and 30 percent of movement respectively, of LR after 16 quarters. 4.6 Interest Rate Channel and Exogenous Shocks in the Case of Qatar Figure 4 illustrates the output, price, M2 and NEER impulse response function for Qatar, subsequent to an increase in lending rate. Positive innovation in LR by approximately 30 basis points causes output and price in Qatar to decline. Output bottoms out after 7 quarters and price continuously and significantly decreases after shock. Table 6 presents variance decomposition for each variable. A shock to WOP is responsible for 8 percent fluctuation of output after 2 and 4 quarters, while 8 percent GDP movement is related to FFR shock after 16 quarters. In addition, 11 and 19 percent GDP fluctuations are related to M2 shocks after 8 and 16 quarters respectively. LR contributes to 7 and 10 percent movement of output and 6 and 16 percent fluctuation of CPI after 8 and 16 quarters, respectively. This means that monetary policy shock affects price level more than output in the long run. Furthermore, LR accounts for 26 percent of the monetary aggregate movement after 16 quarters and 32 percent fluctuation of NEER after 4 quarters. Shocks in output, price and NEER contribute the main effects on LR. NEER shock accounts for 14 percent LR fluctuation after 2 quarters; meanwhile, GDP and CPI contribute 37 and 19 percent movement of output, respectively, after 4 quarters. Finally, FFR accounts for 44 and 40 percent LR fluctuation after 4 and 8 quarters respectively. 4.7 Interest Rate Channel and Exogenous Shocks in the Case of Saudi Arabia Figure 5 displays the impulse response functions of output and price for Saudi Arabia after an increase in interest rate. Positive innovation in TB (Treasury-Bill) by approximately 14 basis points following monetary contraction causes the output in Saudi Arabia to fall. The peak impact occurs by 0.010 percent, 16 quarters after the shock and response which are both long lasting and statistically significant. The price level response shows the price puzzle. Prices do not begin to decline until about 2 quarters, following which they go up before falling in a humpshape. As reported in Table 7, a shock to WOP accounts for 41 and 43 percent GDP fluctuation after 2 and 4 quarters respectively, while 10 and 24 percent GDP fluctuation is related to FFR shocks respectively after 8 and 16 quarters, in addition to 22 and 31 percent GDP fluctuation due to monetary aggregate shocks after 8 and 16 quarters correspondingly. Thus, there is 202
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significant fluctuation in the movement of economic activity as a systematic reaction to monetary aggregate and foreign stocks in the form of world oil price and foreign interest rates. TB contributes 9 and 6 percent of movement in output level and price level respectively after 16 quarters, indicating that interest rate innovation is a relatively weak determinant of fluctuations in economic activity. Furthermore, TB also accounts for 15 percent of monetary aggregate movement after 16 quarters, and 10 percent after 8 quarters. Moreover, 23 and 24 percent fluctuations of NEER relate to TB innovation after 2 and 4 quarters respectively. The opposite is not the case, where a shock in output, price, monetary aggregate, as well as exchange rate do not contribute major effects on interest rate. GDP contributes 8 percent TB fluctuation after 16 quarters and NEER accounts for 10 percent movement of TB after 2 quarters. Our results also show that FFR accounts for 79 and 53 percent fluctuation of TB respectively after 8 and 16 quarters.
V. CONCLUSION Economists and monetary authorities believe that among several problems encountered when implementing their monetary policy is the ambiguity regarding the effect of monetary policy on the various transmission mechanism channels. Such ambiguity might weaken monetary policy efficiency. Since 1981 GCC countries have focused on higher economic integration. Similarity in the transmission mechanism of monetary policy across the GCC states could be an important symbol for further monetary and economic integration amongst these Arab countries. The current paper studied monetary policy transmission throughout five GCC members. SVAR models were employed for estimating GCC monetary and foreign shock, models that had so far been successfully applied to a number of economies (Kim and Roubini 2000). The results obtained from this study show that the response of GCC’s GDP to interest rate shock is immediate. In some GCC countries, interest rate responds positively and simultaneously to unexpected positive innovation in the monetary aggregate. It shows monetary policies affect economy and aggregate demand most deeply if monetary authorities accept a flexible exchange rate regime. Therefore, in this situation, the effects of monetary policy shock on exchange rate are increasingly evident. Moreover, this study’s results indicate that significant economic activity fluctuations in the case of GCC countries are related to systematic reactions to foreign stocks. Indeed, a fixed exchange rate is a more suitable regime for domestic nominal shocks. If the countries encounter foreign nominal shocks, a more flexible exchange rate is logical. In this situation, there is more autonomy of monetary policy authority (or sovereignty of the GCC monetary union) on the economics of GCC countries for handling external shocks. Furthermore, in GCC states, while the signs of exogenous effects of symmetric shocks are the same, the strength of these effects varies among countries. Policy makers in the next GCC central bank should be aware of the effects and costs of symmetric and asymmetric shocks on GCC states. Our results also show that the impact of monetary policy in some countries on price (like Kuwait and Qatar) is stronger than in other countries. Thus, setting a common monetary policy and evaluating the cost of the GCC monetary union are important subjects for GCC monetary authorities, as GCC states are already utilizing different monetary instruments and price level is higher in some states. 203
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References Al Raisi, A.H., S. Pattanaik, and A.Y. Al Raisi (2007). Transmission mechanism of monetary policy under the fixed exchange rate regime of Oman. CBO occasional paper, 2007.1. Amisano, G. and C. Giannini (1997). Topics in Structural VAR Econometrics, 2nd edition. Berlin: Springer. Bennaceur, S., A. Boughrara, and S. Ghazouari (2006). On the linkage between monetary policy and MENA stock market. ERF, 13 Annual conference, oil impact on the global economy. Cheung, Y.W. and K.S. Lai (1993). Long-run Purchasing Power Parity during the Recent Float, Journal of International Economics. 34: 181-192. Darrat, A. (1985). The Monetary Explanation of Inflation: The Experience of three Major OPEC Economies, Journal of Economics and Business. 37: 209-21. Denton, F. (1971). Adjustment of monthly or quarterly series to annual totals: an approach based on quadratic minimization, Journal of American Statistical Association. 66: 99-102. Dickey, D.A., and W.A. Fuller (1979). Distribution of Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association. 74: 427-431. Gottschalk, P. (2002). Downward nominal wage flexibility: real or measurement error? Boston College Working Papers in Economics 534, Boston College Department of Economics. Guiso, L., A.K. Kashyap, F. Panetta, and D. Terlizzese (1999). Will a common European monetary policy have asymmetric effects? Federal Reserve Bank of Chicago Economic Perspectives. 23: 56-75. Hall, S.G. (1989). Maximum Likelihood Estimation of Cointegration Vectors: An Example of the Johansen Procedure, Oxford Bulletin of Economics and Statistics. 51: 213-218. Hamilton, J.D. (1994). Time series analysis. New Jersey: Princeton university press. Hassan, M.K., A. Nakibullah (2008). Persian Gulf Monetary Union and Regional Integration. Economic research forum, Working Paper 453. Ibrahim, M.H., 2006. Integration or Segmentation of the Malaysian Equity Market: An Analysis of Pre- and Post-Capital Controls. Journal of the Asia Pacific Economy. 11: 424-443. Johansen, S. (1992). Testing Weak Exogeneity and the Order of Cointegration in UK Money Demand Data, Journal of Policy Modeling. 14: 313-334. Khan, M.S. (2009). The GCC Monetary Union: Choice of Exchange Rate Regime. Peterson Institute for International Economics working paper, 09 – 1. Kim, S. and N. Roubini (2000). Exchange rate anomalies in the industrial countries: A solution with structural VAR approach. Journal of Monetary Economies. 45: 561-586. Lanne, M., H. Lutkepohl, and P. Saikonen (2002). Comparison of unit root tests for time series with level shifts, Journal of time series.12:123-43. Love, I. and L. Zicchino, (2006). Financial development and dynamic investment behavior: evidence from panel VAR, The Quarterly Review of Economics and Finance. 46: 190-210. Phillips, P.C.B. and P. Perron (1988). Testing for a unit root in time series regression, Biometrika. 75: 335–346. Sturm, M., J. Strasky, A. Petra, and D. Peschel (2008). The Persian Gulf Cooperation Council countries economic structures, recent developments and role in the global economy. ECB Occasional Paper No. 92.
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Figure 1: Responses to a Positive Interest Rate Shock-Bahrain
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Figure 2: Responses to Positive Interest Rate Shocks-KU
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Figure 3: Responses to a Positive Interest Rate Shock-oM
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Figure 4: Responses to Positive Interest Rate Shocks-QA
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Figure 5: Responses to a Positive Interest Rate Shock-SA
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Table 1: Contemporaneous Coefficients and Standard Error a21 Standard error a31 Standard error a61 Standard error a71 Standard error a62 Standard error a72 Standard error a43 Standard error a53 Standard error a73 Standard error a54 Standard error a74 Standard error a65 Standard error a75 Standard error a56 Standard error a76 Standard error a47 Standard error a67 Standard error
Bahrain -1.1836 ( 0.2265) -0.0224 (0.0149) 0.1222 (0.0431) -0.6023 (0.5610) -0.0576 (0.5610) 0.0583 (0.0874) -3.8641 (12.6513) 0.0932 (0.2251) 25.4521 (21.1178) 0.0006 (0.3464) 0.2631 (0.2703) -0.0178 (0.0086) -3.7551 (2.6252) -0.0430 (0.0271) 23.4397 (18.0165)
Kuwait -0.8998 ( 0.2470) -0.1251 (0.0293) 0.5480 (0.5588) 0.2945 (0.5246) -1.0503 (0.2574) 0.0068 (0.0519) -0.6206 (1.5134) 0.0431 (0.1536) -0.7165 (1.4063) -3.2325 (1.8493) 9.0477 (16.0115) -1.1532 (4.4626) 1.1502 (2.1923) 0.0096 (0.0195) 0.0370 (0.0667) -9.9204 (24.3732 ) 6.4883 (7.5817)
Oman -1.0224 ( 0.2573) -0.0656 (0.0226)
Qatar -0.8276 ( 0.2330) -0.0647 (0.0394)
0.0682 (0.0317) -0.2240 (0.2228) -0.0332 (0.0123) -0.632 (0.0264) 0.2058 (0.2346) 0.2915 (0.1692) 0.6703 (1.0234) -0.2269 (0.6620) -4.4277 (6.4880) 0.2317 (0.3232) 0.0773 (0.1123) -0.0446 (0.0315)
0.0195 (0.0330) -1.1383 (0.2632) 0.0181 (0.0644) 0.0509 (0.0359) 0.3314 (0.1239) -0.0201 (0.1029) 0.2678 (0.4427) -0.4107 (0.3976) 0.4893 (2.1676) -0.0839 (0.1457) -0.0067 (0.0174) -0.0311 (0.0633)
7.6913 (7.0575 )
11.8472 (15.0900)
Saudi Arabia -0.9322 ( 0.2689) -0.0914 (0.0174) 1.2679 (1.1958) -0.0858 (0.1052) -1.8895 (0.5915) 0.1786 (0.1630) 0.0587 (0.0674) -0.0939 (0.1841) 0.7851 (0.5836) -0.5091 (0.3479) -0.8049 (0.7263) -3.5392 (3.3478) 0.4394 (0.4374) 0.0092 (0.0097) -0.1409 (0.1134) 0.0400 (0.0691) 20.9303 (22.7363)
Table 2: Over Identifying Restrictions Test Countries Bahrain
Kuwait Oman Qatar
Saudi Arabia
210
Chi-Square | 2 (6) = 2.47
Significant level 0.82
| 2 (4) = 2.11
0.71
| 2 (6) = 1.69
0.94
| 2 (6) = 1.72 | (4) = 1.07 2
0.94 0.89
NEER
LR
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.06 0.06 0.08 0.25 0.03 0.05 0.05 0.06 0.05 0.05 0.06 0.06 0.11 0.31 0.41 0.28 0.02 0.02 0.03 0.02
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.44
0.30
0.18
0.14
0.17
0.07
0.03
0.02
0.04
0.04
0.03
0.02
0.01
0.01
0.00
0.00
0.13
0.04
0.01
0.00
FFR
0.02
0.01
0.01
0.00
0.16
0.08
0.03
0.00
0.04
0.03
0.02
0.02
0.09
0.05
0.03
0.03
0.41
0.63
0.82
0.91
GDP
0.08
0.04
0.03
0.01
0.14
0.13
0.10
0.08
0.02
0.02
0.02
0.02
0.72
0.82
0.87
0.89
0.02
0.04
0.04
0.02
CPI
M2
0.06
0.04
0.03
0.01
0.22
0.20
0.10
0.38
0.37
0.38
0.39
0.40
0.06
0.03
0.03
0.04
0.09
0.06
0.02
0.00
Table 3: Variance Decomposition: Basic SVAR Model-BA
0.21
0.30
0.44
0.54
0.13
0.21
0.38
0.49
0.26
0.26
0.27
0.27
0.05
0.03
0.01
0.00
0.07
0.10
0.03
0.01
LR
0.15
0.24
0.25
0.24
0.06
0.05
0.06
0.12
0.21
0.22
0.22
0.22
0.01
0.01
0.01
0.01
0.04
0.05
0.02
0.00
NEER
Sayyed Mahdi Ziaei
211
212
NEER
MMR
M2
CPI
GDP
VARIANCE DECOMPOSITION
0.06 0.05 0.10 0.02 0.04 0.03
4
8
16
2
4
8
0.10
0.02
2
16
0.07
16
0.04
0.13
8
8
0.08
4
0.03
0.05
2
4
0.17
16
0.02
0.20
8
2
0.28
4
0.07
0.29
2
16
WOP
PERIOD
0.15
0.11
0.02
0.03
0.45
0.53
0.39
0.28
0.09
0.01
0.01
0.03
0.01
0.01
0.01
0.00
0.23
0.08
0.01
0.00
FFR
0.02
0.02
0.02
0.03
0.08
0.06
0.03
0.01
0.29
0.23
0.07
0.03
0.36
0.26
0.19
0.11
0.22
0.52
0.63
0.65
GDP
0.21
0.20
0.20
0.14
0.07
0.03
0.02
0.03
0.01
0.01
0.02
0.00
0.32
0.46
0.54
0.69
0.01
0.01
0.03
0.04
CPI
Table 4: Variance Decomposition: Basic VAR Model-KU
0.37
0.19
0.05
0.01
0.06
0.03
0.02
0.01
0.45
0.63
0.76
0.82
0.20
0.06
0.03
0.00
0.25
0.10
0.01
0.00
M2
0.03
0.08
0.11
0.14
0.26
0.29
0.39
0.48
0.07
0.03
0.02
0.02
0.03
0.05
0.10
0.10
0.07
0.05
0.04
0.01
MMR
0.12
0.36
0.57
0.65
0.01
0.03
0.11
0.17
0.02
0.04
0.06
0.08
0.01
0.02
0.05
0.05
0.05
0.04
0.03
0.01
ER Evaluating the Effects of Monetary Policy Shocks on GCC Countries
NEER
LR
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.15 0.12 0.07 0.04 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.07 0.06 0.04 0.04 0.12 0.12 0.10 0.05
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.16
0.06
0.07
0.09
0.31
0.33
0.08
0.00
0.13
0.17
0.03
0.00
0.07
0.06
0.00
0.00
0.02
0.00
0.00
0.00
FFR
0.13
0.08
0.05
0.04
0.13
0.15
0.03
0.00
0.24
0.07
0.01
0.03
0.28
0.21
0.18
0.09
0.76
0.80
0.81
0.82
GDP
0.36
0.06
0.00
0.00
0.30
0.12
0.02
0.01
0.04
0.03
0.05
0.05
0.51
0.62
0.76
0.88
0.01
0.01
0.00
0.00
CPI
Table 5: Variance Decomposition: Basic VAR Model-OM
0.03
0.01
0.01
0.02
0.10
0.10
0.24
0.27
0.06
0.18
0.41
0.59
0.03
0.01
0.01
0.01
0.02
0.01
0.00
0.00
M2
0.15
0.37
0.32
0.19
0.06
0.16
0.42
0.48
0.49
0.44
0.33
0.18
0.08
0.06
0.02
0.01
0.09
0.04
0.02
0.01
LR
0.13
0.32
0.43
0.54
0.06
0.10
0.15
0.18
0.03
0.09
0.15
0.14
0.02
0.02
0.03
0.01
0.07
0.08
0.05
0.01
NEER
Sayyed Mahdi Ziaei
213
214
NEER
LR
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.08 0.08 0.05 0.03 0.02 0.01 0.03 0.09 0.00 0.00 0.01 0.11 0.09 0.04 0.04 0.08 0.02 0.02 0.04 0.02
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.09
0.01
0.00
0.01
0.23
0.40
0.44
0.33
0.07
0.16
0.13
0.04
0.06
0.07
0.02
0.01
0.08
0.03
0.01
0.00
FFR
0.04
0.04
0.01
0.00
0.37
0.19
0.05
0.01
0.02
0.03
0.02
0.00
0.01
0.01
0.01
0.02
0.53
0.70
0.84
0.91
GDP
0.30
0.17
0.09
0.06
0.19
0.19
0.13
0.03
0.11
0.12
0.03
0.00
0.58
0.73
0.89
0.93
0.03
0.00
0.00
0.00
CPI
Table 6: Variance Decomposition: Basic VAR Model-QA
0.14
0.11
0.05
0.02
0.02
0.02
0.01
0.00
0.33
0.57
0.80
0.94
0.05
0.04
0.02
0.01
0.19
0.11
0.02
0.00
M2
0.22
0.29
0.32
0.32
0.05
0.09
0.25
0.40
0.26
0.04
0.01
0.00
0.16
0.06
0.02
0.00
0.10
0.07
0.02
0.00
LR
0.19
0.34
0.51
0.66
0.05
0.05
0.08
0.14
0.10
0.08
0.01
0.00
0.07
0.05
0.02
0.01
0.06
0.04
0.03
0.01
NEER Evaluating the Effects of Monetary Policy Shocks on GCC Countries
NEER
TB
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.41 0.43 0.29 0.14 0.01 0.01 0.01 0.03 0.01 0.01 0.02 0.01 0.04 0.02 0.02 0.12 0.03 0.02 0.06 0.09
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.21
0.06
0.03
0.03
0.53
0.79
0.85
0.74
0.10
0.02
0.01
0.01
0.07
0.04
0.04
0.02
0.24
0.10
0.04
0.01
FFR
0.05
0.03
0.01
0.01
0.08
0.02
0.03
0.05
0.08
0.04
0.01
0.01
0.04
0.05
0.08
0.08
0.08
0.19
0.30
0.55
GDP
0.07
0.03
0.02
0.00
0.06
0.03
0.01
0.00
0.07
0.12
0.12
0.09
0.67
0.73
0.79
0.86
0.08
0.08
0.03
0.00
CPI
Table 7: Variance Decomposition: Basic VAR Model-SA
0.05
0.03
0.02
0.01
0.07
0.05
0.01
0.02
0.51
0.65
0.78
0.83
0.08
0.05
0.02
0.01
0.31
0.22
0.09
0.01
M2
0.11
0.19
0.24
0.23
0.08
0.03
0.03
0.05
0.15
0.10
0.05
0.04
0.06
0.04
0.02
0.01
0.09
0.08
0.04
0.01
TB
0.42
0.59
0.67
0.68
0.05
0.06
0.05
0.10
0.08
0.05
0.02
0.01
0.09
0.07
0.04
0.01
0.06
0.04
0.04
0.01
NEER
Sayyed Mahdi Ziaei
215
Evaluating the Effects of Monetary Policy Shocks on GCC Countries Sayyed Mahdi Ziaei Department of Business Administration Faculty of Management Universiti Teknologi Malayisa (UTM), Skudai Malaysia Email: [email protected] Abstract:
For the first time, this research assesses monetary policy shock effects on GCC members over the last 17 years using a structural vector autoregressive (SVAR) model baseline. While GCC states peg their currency to the US dollar, the contemporaneous coefficient in the structural model indicates that for GCC countries a monetary policy instrument responds positively to unexpected increases in M2, while a monetary aggregate reacts negatively to interest rate shocks. However, our findings indicate that these countries’ interest rate channel is weak. Furthermore, oil price innovation contributes to most output fluctuations in the short horizon, and M2 and Federal Fund Rates shocks are responsible for most output movements in the long horizon.
I. Introduction The objective of this paper is to determine how the Persian Gulf Cooperation Council (hereafter referred to as GCC) economies, namely Bahrain, Kuwait, Oman, Qatar and Saudi Arabia respond to monetary policy shocks. One of the key monetary policy strategies in GCC countries is price stability, but since 2002, inflation in these countries has been increasing. The GCC countries’ oil price boom by 2002 was accompanied by inflation pressure. In fact, inflation decreases the purchasing power of consumers. Meanwhile, from 2001 the dollar has been losing value against major currencies. Depreciating dollar value induces import cost increases for the countries whose imports are mostly from the EU, Japan and China (Sturm et al., 2008), meaning that GCC’s pegged currencies are accompanied by lower purchasing power. Furthermore, the new millennium has witnessed GCC monetary authorities face complicated situations in using interest rate to control inflation. The dropping US interest rate has imposed additional inflationary pressure in GCC countries. However, in order to maintain their parity with US interest rate and control capital inflow GCC countries’ monetary authorities have brought down their interest rates. Furthermore, inflation in GCC countries has decreased the real interest rate in these states in comparison to the higher interest rate levels in the mid-1990s with lower inflation levels. 195
Evaluating the Effects of Monetary Policy Shocks on GCC Countries
From a theoretical point of view, because GCC exchange rates are fixed to the US dollar, it seems that GCC monetary policies and interest rates should follow US Federal Reserve policies; but divergent business cycles between the US and GCC (which have slowed down during the US and GCC economic boom before the 2009 worldwide recession1) have raised questions regarding the roles of monetary policies and foreign shocks in aggregate demand of GCC countries. Furthermore, as Guiso et al. (1999) emphasized there are at least three key conditions that must be achieved for a common monetary policy (like a GCC monetary union) to succeed among union members. First, members must accept the final targets of a common monetary policy. Second, the monetary policy transmission mechanism (MTM) effects should be identical among monetary union members. Differences in MTM impact may potentially hinder the assessment of appropriate monetary policy decisions. Finally, if member countries’ inflation and business cycles are similar, chances of implementing common monetary policies increase. In this paper, structural VARs (SVARs) models are employed to evaluate the effects of monetary policy shocks or interest rate changes on output and inflation of GCC countries. The remainder of this paper is organized as follows. In the next section, the literature on GCC monetary policy is reviewed. In Section III we present the econometric SVAR model and a baseline SVAR model that describes how the GCC monetary policy is constructed. In section IV empirical results achieved by SVAR estimation are analyzed. In the last section, results are summarized and various implications are presented.
II. LITERATURE REVIEW There are several theories that analyze the effects of monetary policy on economy. The standard, neoclassical models consider money neutral, implying that a tightening or expansionary monetary policy together with a decrease or increase in money supply would only affect nominal variables but not real variables. In contrast, according to the Keynesian IS/LM model, (money view) an expansionary monetary policy would induce a fall in short-term interest rate. Since the long-term interest rate is the average of the expected future short-term interest rate, by decreasing short-term interest rate, long-term interest rate drops. A declining interest rate causes various types of investment including real estate, business and inventory investment to increase, and also boosts consumption expenditures. Rising investment and consumption result in mounting aggregate demand. There has been relatively little research done on the monetary policy of GCC states. For example, Darrat (1985) evaluated the impact of money on price level fluctuation in Libya, Nigeria and Saudi Arabia. Findings indicate that lower, real income growths and higher money supplies are linked with higher inflation in the three countries. Bennaceur et al. (2006) analyzed the effects of monetary policy transmission on the stock market in nine MENA countries, inclusive of Bahrain, Oman and Saudi Arabia. It was figured out that monetary tightening seems to have a significant impact on stock market return in the case of Bahrain, 1
196
GCC economies after 7 years of oil price boom encounter oil price decline and worldwide recession in mid of 2008 and 2009. This worldwide recession accompanies with restrictive monetary and fiscal policy (monetary union treaty commitment) accelerate the slowdown of these countries’ economies.
Sayyed Mahdi Ziaei
Egypt, and Saudi Arabia. Al-Raisi et al. (2007) assessed the significance of monetary policy independence with regards to Oman. Their VAR analysis suggests that inflation responds to monetary variables like interest rate and money growth but the effect is not permanent. The main reason behind the weakness of monetary transmission in Oman is the pegging of the exchange rate to the US dollar. Hassan and Nakibullah (2008) tried to present evidence on whether the capital markets amongst GCC countries and the US are completely integrated. Evidence overwhelmingly indicates that capital is not perfectly mobile between these countries, implying that the monetary authorities of the GCC countries have had some room to the stage management of their monetary policy.
III. SVAR MODEL In this research, a baseline SVAR model with contemporaneous restrictions was selected in order to analyze the effects of monetary policy and foreign shocks on the aggregate demand of GCC states. For VAR analysis, the following structural equation is first assumed:
A0Yt = At X t + Bε t
(1)
Yt = A∗ Xt + ut
(2)
where Yt is the (n × 1) vector of endogenous variables, A0 is a (n × n) matrix of coefficients of simultaneous relations on the endogenous variables, Xt includes the lag of endogenous variables, A is the matrix of coefficients on the lagged variables in the model, εt as a (n × 1) vector of structural innovation is orthogonal, and ∑ = E (ε tε t' ) presents the varianceεt covariance matrix of the structural innovation. Furthermore, εt is orthogonal and has normal distribution, meaning that shocks are uncorrelated and the variance-covariance matrix has a normal distribution with zero mean. The main difficulty with evaluating the structural model is that the real values of A0 and A cannot be directly estimated. Data sampling information is not adequate for identifying additional restrictions. Gottschalk (2001) believed there are too many sets of unlike values of A0 and A, all of which indicate a similar probability distribution of data. This matter estimates the real value for which A0 and is impossible. To resolve this issue, we should obtain a reduced form of model (1), which would explain each endogenous variable exclusively as a function of predetermined variables: With A∗ = A0−1 A and ut = A0−1 Bε t In this situation, in order to recover the structural parameters from the reduced form model or to exactly identify the model as Hamilton (1994) mentioned, the order condition should be satisfied. It means the number of parameters in the covariance matrix of the reduced form should be the same. The variance-covariance matrix of the reduced form is given by:
∑ = E (u u ) or ∑ = (A )∑ u
t
'
u
−1 0
εt
(A0−1 )'
(3)
To achieve identification, it is necessary for the parameters in B and A0 to be recoverable from the reduced form. In Eq. (3), ∑ contains K(K +1) ⁄ 2 parameters, and there are K(K+1) 197
Evaluating the Effects of Monetary Policy Shocks on GCC Countries
free parameters in the right side of Eq. (3), so we should impose the 2K2 – K – K(K + 1) ⁄ 2 restriction on B and A0. Because normally the K(K –1) restriction in imposed on B to be diagonal, identification is achieved if at least the K(K – 1) ⁄ 2restriction is imposed on A0. In the VAR modeling with Cholesky decomposition, A0 is considered triangular. However, in structural VAR, A0 can be any structure until it has sufficient restrictions. 3.1. The GCC Countries’ SVAR Models In this research, a non-recursive SVAR framework was applied to analyze the monetary transmission mechanism in GCC states. Structural shocks in SVAR can be identified by inserting some restrictions in the baseline model. The SVAR basic model of the GCC countries’ economies consists of seven variables represented by the following vector, Xt : Xt = (OPWt , FFRt ,GDPt ,CPI t , M t , Rt , Et )
(4)
where OPWt is the world oil price in US dollars, FFRt is the US federal funds rate, GDPt is the gross domestic production, CPIt is the consumer price index, Mt is the monetary aggregate (M2), Rt is the monetary policy instrument, and Et is the nominal effective exchange rate2. In identifying the structural VAR, the Amisano and Gianini (1997) strategy (AB method) was utilized. In this method enough restrictions are imposed on both matrices A0 and B. For the system to be justly identified, it requires 2n 2 − n(n +1) / 2 or 70 restrictions on both A0 and B. Since 42 restrictions are imposed on B (assumed to be a diagonal matrix in the model), another 28 restrictions on A0 are required for the system to be justly identified. The restrictions placed on the contemporaneous relationships among the variables are shown in Equation (5). In the left side of the baseline SVAR model, coefficients bij point out that variable j immediately affects variable i. The identified system A0ut = Bε t is as follows: ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢⎣
1 a21
a31 0
0
0
0 1
0 0
0
a43
a62
0
0 0
a71 a72
1
0 0
0 0
1
0
0
a65
0
a53
a54
a73
a74
0
0 0 0
0
1
a56
a75
a76
1
0 0 0
0
0
a67 1
⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎦ ⎢ ⎣
⎤ utOPW ⎥ ⎡ 1 utFFR ⎥ ⎢ ⎥ ⎢ 0 utGDP ⎥ ⎢ 0 ⎥ = utCPI ⎥ ⎢⎢ 0 utM ⎥ ⎢ 0 ⎥ ⎢ 0 utR ⎥ ⎢ 0 ⎥ ⎣ utE ⎥⎦
0 1 0 0 0 0 0
0 0 1 0 0 0 0
0 0 0 1 0 0 0
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1
⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎦ ⎢ ⎢⎣
ε tOPW ⎤⎥ ε tFFR ⎥ ⎥ ε tGDP ⎥ ⎥ ε tCPI ⎥ ε tM ⎥ ⎥ ε tR ⎥ ⎥ ε tE ⎥⎦
(5)
The first two variables are US interest rate and world oil price, representing the exogenous external shocks. Domestic variable shocks have no effect on these two variables contemporaneously. However, it is expected that the Federal Funds Rate will immediately react positively to the increase in oil price, because US monetary authorities use a tightening policy when they encounter oil price shocks. The interest rate, exchange rate and money aggregate seem to affect the level of output with lag delay. The price level responds instantly to the output 2
198
Except in the case of Kuwait, that nominal exchange rate is used.
Sayyed Mahdi Ziaei
for all GCC countries, nominal effective exchange rate is only for Saudi Arabia and exchange rate is for Kuwait. The reason for this is that the exchange rate is one of the main indicators of price stability adjustment in the instance of Saudi Arabia and Kuwait. Furthermore, as the government determines oil price to be below the international level, domestic price is not supposed to simultaneously react to the oil price. As GCC countries form the main exporter bloc of oil and petrol, the dollar plays a crucial role in these countries’ economies. GDP is projected to respond positively to world oil prices. The fifth variable represents the demand for a money relation, where the demand for money is assumed to react concurrently to the short-term interest rate, price level and output. The sixth variable represents the money supply. The monetary authority sets the interest rate after contemplating the current oil price values (only for Saudi Arabia and Kuwait), money, exchange rate, and more importantly the interest rate of anchor countries as well as the lagged values of all variables in Xt. The exchange rate is included in the equation because of its pass-through effect. Money and exchange rate are entered in the interest rate function contemporaneously because these variables’ data is available instantaneously. Nevertheless, the data of important variables like output and price level is not readily available to monetary authorities, thus such variables are not included in the reaction function of monetary authority. It is anticipated that interest will respond positively to nominal effective exchange rate and money aggregate. Finally, because exchange rate plays the role of a forward-looking asset price (Kim & Roubini 2000), it is assumed that this variable reacts simultaneously to all other variables and describes financial market equilibrium. 3.2. Data and Choice of Variables We examined the relationship between the monetary policy variable and both output and price in five GCC countries, namely Bahrain, Kuwait, Oman, Qatar and Saudi Arabia, using a SVAR method. Quarterly data from 1992Q4 to 2009Q4 was employed in this study. OPW is the world oil price in terms of US dollars, and FFR is the US federal fund rate. Variables OPW and FFR are determined exogenously relative to policy shock. They serve as instruments to isolate exogenous monetary policy shocks. With respect to interest rate the money market rate (MMR) for Kuwait, bank lending rate (LR) for Bahrain, Oman and Qatar, and three month treasury bill (TB) for Saudi Arabia are used as the key, short-term interest rate employed by these countries’ central banks to signal their monetary policy stance. Nominal effective exchange rate (NEER) is used to examine the influences of exchange rate changes on output and price. We applied NEER as opposed to GCC exchange rate (except in the case of Kuwait) because the exchange rate of GCC countries is pegged to the USD over the respective time period. In this way, while the exchange rate is not included in the model (except for Kuwait), the exchange rate fluctuations that may have inflationary effects are captured by NEER. M2 serves as a proxy of monetary aggregate. Output is measured as real GDP due to the lack of quarterly data on the GCC GDP, the Proportional Denton Method is used to extract quarterly GDP and the consumer price index (CPI) is taken as the measure of the general price level. All data is expressed in natural logs except for the interest rates which are shown in levels. Oil price boom effects (rising oil price began in 2002) are considered by the oil dummy in each country’s estimation process. 199
Evaluating the Effects of Monetary Policy Shocks on GCC Countries
IV. SVAR EMPIRICAL RESULTS In this section the contemporaneous coefficients in the structural model are estimated, after which the impulse responses and variance decompositions to a domestic tightening monetary shock are presented. In the final part the effects of monetary policy shocks on domestic variables are discussed. 4.1 Lag Length Although different information criteria can be used to specify lag length, some experts like Cheung and Lai (1993) believe that because of the shifting average error term, the choice of lag length with applying information criteria may not achieve suitable results (Ibrahim 2006). Thus, as Hall (1989) and Johansen (1992) proposed, it is possible to determine lag length by testing whether residual is serially uncorrelated in different lag lengths or not. This method is applied for determining lag length in this paper. The model for Kuwait is estimated using 3 lags of quarterly data, while 2 lags are included for Bahrain, Oman and Saudi Arabia and Qatar. 4.2 Contemporaneous Coefficients in GCC Countries’ Structural Models Table 1 presents the contemporaneous coefficients and standard error in the structural model for each GCC country. The main results of the contemporaneous coefficients indicate the following. In the cases of Saudi Arabia, Kuwait and Oman, the contemporaneous effects of monetary aggregate on interest rate are negative. In other words, the interest rate of these countries responds positively to unexpected increases in M2. Moreover, for Saudi Arabia, Kuwait and Oman, the monetary aggregate decreases at the same time as an unexpected increase in interest rate. Regarding Kuwait and Saudi Arabia the contemporaneous effects of oil price on interest rate are negative. This demonstrates that with an increase in oil price, interest rate in these countries decreases instantly. In addition, the contemporaneous effect of oil price on aggregate demand is negative, implying that unexpected changes in oil price cause GDP in all GCC countries to increase. For all GCC countries the simultaneous effect of federal fund rate on interest rate is negative, which emphasizes that these countries’ interest rates have a positive reaction to federal fund rates. 4.3 Interest Rate Channel and Exogenous Shocks for Bahrain In this section, the effects of the monetary policy instrument on aggregate demand and variables of the financial market in the case of Bahrain are assessed. Figure 1 shows the impulse response functions for output and price along with 95% confidence intervals for Bahrain. Positive innovations in LR (lending rate) following a monetary contraction cause the output in Bahrain to fall. The output bottoms out after 7 quarters by 0.010 percent. Also, the price falls instantly, but after one quarter it starts to rise. Table 3 illustrates the variance decompositions for all variables in the baseline model at forecast horizons of 2 through 16 quarters, which explains the percentage fluctuations in a 200
Sayyed Mahdi Ziaei
given variable caused by different shocks. As reported in Table 3, a shock to WOP accounts for 6 percent fluctuation of GDP after 2 quarters and 25 percent after 16 quarters. LR contributes 10 and 7 percent of movement in output, and 5 and 3 percent fluctuation of price, respectively after 8 and 16 quarters. Moreover, 13 fluctuations of output are related to FFR and 9 percent movement of output is due to M2 after 16 quarters. In addition, 54 and 44 percent fluctuation of NEER and 27 percent movement of M2 after 2 and 4 quarters, respectively, are due to LR shock. Interestingly, shocks in all variables contribute to LR fluctuation. Oil price, FFR, GDP, CPI and M2 account for 28, 17, 16, 14 and 22 percent fluctuations in LR after 16 quarters, respectively. Furthermore, NEER accounts for 12 and 6 percent movement of LR after 2 and 4 quarters, respectively. 4.4 Interest Rate Channel and Exogenous Shocks in the Case of Kuwait Figure 2 illustrates the impulse response functions for Kuwait’s output, price, M2 and ER (nominal exchange rate) following an increase in money market rate. Positive innovation in MMR (money market rate) by approximately 44 basis points following monetary contraction causes output in Kuwait to fall after 1 quarter. Output declines by 0.016 percent after 16 quarters, meanwhile price level responds immediately to MMR shock. Price falls in 1 quarter and rises after that. Table 4 clarifies variance decomposition for each variable. As reported in this table, a shock to WOP accounts for 29 and 17 percent fluctuation of GDP after 8 and 16 quarters, respectively. Meanwhile, 23 and 25 percent fluctuation of GDP is related to FFR and monetary aggregate shocks respectively, after 16 quarters. So this means that the significant fluctuation in output is related to monetary aggregate and foreign shocks in the long term. MMR contributes to 7 percent movement in output level after 16 quarters and 10 percent fluctuation in price level after 2 and 4 quarters. This result indicates that interest rate innovations are weak determinants of output movements. While interest rate shock contributes to price movement effectively in the short term, GDP shock accounts for 36 percent fluctuation of price level after 16 quarters. Furthermore, the MMR accounts for 14 and 11 percent ER movements after 2 and 4 quarters, respectively. Results also show that FFR accounts for 53 and 43 percent fluctuations of MMR after 8 and 16 quarters respectively. In addition, 17 and 11 percent fluctuations of MMR after 2 and 4 quarters respectively, are attributed to exchange rate shocks. 4.5 Interest Rate Channel and Exogenous Shocks in the Case of Oman The impulse response function of output, price, nominal effective exchange rate and monetary aggregate following a monetary tightening is provided in Figure 3. A positive innovation in LR by approximately 10 basis points following a tightening of monetary policy induces an output and price drop in Oman. After 16 quarters, output falls 0.02 percent and price bottoms out at 12 quarters after the shock. Table 5 demonstrates variance decomposition for each variable. It is evident that a shock to WOP accounts for 15 and 12 percent of GDP fluctuations after 2 and 4 quarters respectively, while 8 and 7 percent of GDP fluctuation relates to NEER after 8 and 16 quarters respectively. LR contributes to 9 and 8 percent of GDP fluctuation and CPI respectively, after 16 quarters. 201
Evaluating the Effects of Monetary Policy Shocks on GCC Countries
These outcomes show that interest rate innovation is a weak element of movement in economic activity. In spite of the fact that interest rate shock contributes to price movement in the long run, the main fluctuations of price level are due to output shock. 28 percent fluctuation of price after 16 quarters is related to output shock. Furthermore, LR accounts for 33 and 44 percent movement of monetary aggregate and 32 and 37 percent fluctuation of NEER after 4 and 8 quarters, respectively. Moreover, a shock in all variables contributes the main effect on interest rate. NEER, M2 and past shocks of LR account for the main effects on LR in the short term and GDP, CPI and FFR account for the main effect on LR in the long run. NEER contributes 18 and 15 percent while M2 contributes 27 and 24 percent movement of LR after 2 and 4 quarters, respectively. Moreover, FFR, GDP and CPI account for 31, 13 and 30 percent of movement respectively, of LR after 16 quarters. 4.6 Interest Rate Channel and Exogenous Shocks in the Case of Qatar Figure 4 illustrates the output, price, M2 and NEER impulse response function for Qatar, subsequent to an increase in lending rate. Positive innovation in LR by approximately 30 basis points causes output and price in Qatar to decline. Output bottoms out after 7 quarters and price continuously and significantly decreases after shock. Table 6 presents variance decomposition for each variable. A shock to WOP is responsible for 8 percent fluctuation of output after 2 and 4 quarters, while 8 percent GDP movement is related to FFR shock after 16 quarters. In addition, 11 and 19 percent GDP fluctuations are related to M2 shocks after 8 and 16 quarters respectively. LR contributes to 7 and 10 percent movement of output and 6 and 16 percent fluctuation of CPI after 8 and 16 quarters, respectively. This means that monetary policy shock affects price level more than output in the long run. Furthermore, LR accounts for 26 percent of the monetary aggregate movement after 16 quarters and 32 percent fluctuation of NEER after 4 quarters. Shocks in output, price and NEER contribute the main effects on LR. NEER shock accounts for 14 percent LR fluctuation after 2 quarters; meanwhile, GDP and CPI contribute 37 and 19 percent movement of output, respectively, after 4 quarters. Finally, FFR accounts for 44 and 40 percent LR fluctuation after 4 and 8 quarters respectively. 4.7 Interest Rate Channel and Exogenous Shocks in the Case of Saudi Arabia Figure 5 displays the impulse response functions of output and price for Saudi Arabia after an increase in interest rate. Positive innovation in TB (Treasury-Bill) by approximately 14 basis points following monetary contraction causes the output in Saudi Arabia to fall. The peak impact occurs by 0.010 percent, 16 quarters after the shock and response which are both long lasting and statistically significant. The price level response shows the price puzzle. Prices do not begin to decline until about 2 quarters, following which they go up before falling in a humpshape. As reported in Table 7, a shock to WOP accounts for 41 and 43 percent GDP fluctuation after 2 and 4 quarters respectively, while 10 and 24 percent GDP fluctuation is related to FFR shocks respectively after 8 and 16 quarters, in addition to 22 and 31 percent GDP fluctuation due to monetary aggregate shocks after 8 and 16 quarters correspondingly. Thus, there is 202
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significant fluctuation in the movement of economic activity as a systematic reaction to monetary aggregate and foreign stocks in the form of world oil price and foreign interest rates. TB contributes 9 and 6 percent of movement in output level and price level respectively after 16 quarters, indicating that interest rate innovation is a relatively weak determinant of fluctuations in economic activity. Furthermore, TB also accounts for 15 percent of monetary aggregate movement after 16 quarters, and 10 percent after 8 quarters. Moreover, 23 and 24 percent fluctuations of NEER relate to TB innovation after 2 and 4 quarters respectively. The opposite is not the case, where a shock in output, price, monetary aggregate, as well as exchange rate do not contribute major effects on interest rate. GDP contributes 8 percent TB fluctuation after 16 quarters and NEER accounts for 10 percent movement of TB after 2 quarters. Our results also show that FFR accounts for 79 and 53 percent fluctuation of TB respectively after 8 and 16 quarters.
V. CONCLUSION Economists and monetary authorities believe that among several problems encountered when implementing their monetary policy is the ambiguity regarding the effect of monetary policy on the various transmission mechanism channels. Such ambiguity might weaken monetary policy efficiency. Since 1981 GCC countries have focused on higher economic integration. Similarity in the transmission mechanism of monetary policy across the GCC states could be an important symbol for further monetary and economic integration amongst these Arab countries. The current paper studied monetary policy transmission throughout five GCC members. SVAR models were employed for estimating GCC monetary and foreign shock, models that had so far been successfully applied to a number of economies (Kim and Roubini 2000). The results obtained from this study show that the response of GCC’s GDP to interest rate shock is immediate. In some GCC countries, interest rate responds positively and simultaneously to unexpected positive innovation in the monetary aggregate. It shows monetary policies affect economy and aggregate demand most deeply if monetary authorities accept a flexible exchange rate regime. Therefore, in this situation, the effects of monetary policy shock on exchange rate are increasingly evident. Moreover, this study’s results indicate that significant economic activity fluctuations in the case of GCC countries are related to systematic reactions to foreign stocks. Indeed, a fixed exchange rate is a more suitable regime for domestic nominal shocks. If the countries encounter foreign nominal shocks, a more flexible exchange rate is logical. In this situation, there is more autonomy of monetary policy authority (or sovereignty of the GCC monetary union) on the economics of GCC countries for handling external shocks. Furthermore, in GCC states, while the signs of exogenous effects of symmetric shocks are the same, the strength of these effects varies among countries. Policy makers in the next GCC central bank should be aware of the effects and costs of symmetric and asymmetric shocks on GCC states. Our results also show that the impact of monetary policy in some countries on price (like Kuwait and Qatar) is stronger than in other countries. Thus, setting a common monetary policy and evaluating the cost of the GCC monetary union are important subjects for GCC monetary authorities, as GCC states are already utilizing different monetary instruments and price level is higher in some states. 203
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References Al Raisi, A.H., S. Pattanaik, and A.Y. Al Raisi (2007). Transmission mechanism of monetary policy under the fixed exchange rate regime of Oman. CBO occasional paper, 2007.1. Amisano, G. and C. Giannini (1997). Topics in Structural VAR Econometrics, 2nd edition. Berlin: Springer. Bennaceur, S., A. Boughrara, and S. Ghazouari (2006). On the linkage between monetary policy and MENA stock market. ERF, 13 Annual conference, oil impact on the global economy. Cheung, Y.W. and K.S. Lai (1993). Long-run Purchasing Power Parity during the Recent Float, Journal of International Economics. 34: 181-192. Darrat, A. (1985). The Monetary Explanation of Inflation: The Experience of three Major OPEC Economies, Journal of Economics and Business. 37: 209-21. Denton, F. (1971). Adjustment of monthly or quarterly series to annual totals: an approach based on quadratic minimization, Journal of American Statistical Association. 66: 99-102. Dickey, D.A., and W.A. Fuller (1979). Distribution of Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association. 74: 427-431. Gottschalk, P. (2002). Downward nominal wage flexibility: real or measurement error? Boston College Working Papers in Economics 534, Boston College Department of Economics. Guiso, L., A.K. Kashyap, F. Panetta, and D. Terlizzese (1999). Will a common European monetary policy have asymmetric effects? Federal Reserve Bank of Chicago Economic Perspectives. 23: 56-75. Hall, S.G. (1989). Maximum Likelihood Estimation of Cointegration Vectors: An Example of the Johansen Procedure, Oxford Bulletin of Economics and Statistics. 51: 213-218. Hamilton, J.D. (1994). Time series analysis. New Jersey: Princeton university press. Hassan, M.K., A. Nakibullah (2008). Persian Gulf Monetary Union and Regional Integration. Economic research forum, Working Paper 453. Ibrahim, M.H., 2006. Integration or Segmentation of the Malaysian Equity Market: An Analysis of Pre- and Post-Capital Controls. Journal of the Asia Pacific Economy. 11: 424-443. Johansen, S. (1992). Testing Weak Exogeneity and the Order of Cointegration in UK Money Demand Data, Journal of Policy Modeling. 14: 313-334. Khan, M.S. (2009). The GCC Monetary Union: Choice of Exchange Rate Regime. Peterson Institute for International Economics working paper, 09 – 1. Kim, S. and N. Roubini (2000). Exchange rate anomalies in the industrial countries: A solution with structural VAR approach. Journal of Monetary Economies. 45: 561-586. Lanne, M., H. Lutkepohl, and P. Saikonen (2002). Comparison of unit root tests for time series with level shifts, Journal of time series.12:123-43. Love, I. and L. Zicchino, (2006). Financial development and dynamic investment behavior: evidence from panel VAR, The Quarterly Review of Economics and Finance. 46: 190-210. Phillips, P.C.B. and P. Perron (1988). Testing for a unit root in time series regression, Biometrika. 75: 335–346. Sturm, M., J. Strasky, A. Petra, and D. Peschel (2008). The Persian Gulf Cooperation Council countries economic structures, recent developments and role in the global economy. ECB Occasional Paper No. 92.
204
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Figure 1: Responses to a Positive Interest Rate Shock-Bahrain
205
EVAluAting thE EffEcts of monEtAry Policy shocks on gcc countriEs
Figure 2: Responses to Positive Interest Rate Shocks-KU
206
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Figure 3: Responses to a Positive Interest Rate Shock-oM
207
EVAluAting thE EffEcts of monEtAry Policy shocks on gcc countriEs
Figure 4: Responses to Positive Interest Rate Shocks-QA
208
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Figure 5: Responses to a Positive Interest Rate Shock-SA
209
Evaluating the Effects of Monetary Policy Shocks on GCC Countries
Table 1: Contemporaneous Coefficients and Standard Error a21 Standard error a31 Standard error a61 Standard error a71 Standard error a62 Standard error a72 Standard error a43 Standard error a53 Standard error a73 Standard error a54 Standard error a74 Standard error a65 Standard error a75 Standard error a56 Standard error a76 Standard error a47 Standard error a67 Standard error
Bahrain -1.1836 ( 0.2265) -0.0224 (0.0149) 0.1222 (0.0431) -0.6023 (0.5610) -0.0576 (0.5610) 0.0583 (0.0874) -3.8641 (12.6513) 0.0932 (0.2251) 25.4521 (21.1178) 0.0006 (0.3464) 0.2631 (0.2703) -0.0178 (0.0086) -3.7551 (2.6252) -0.0430 (0.0271) 23.4397 (18.0165)
Kuwait -0.8998 ( 0.2470) -0.1251 (0.0293) 0.5480 (0.5588) 0.2945 (0.5246) -1.0503 (0.2574) 0.0068 (0.0519) -0.6206 (1.5134) 0.0431 (0.1536) -0.7165 (1.4063) -3.2325 (1.8493) 9.0477 (16.0115) -1.1532 (4.4626) 1.1502 (2.1923) 0.0096 (0.0195) 0.0370 (0.0667) -9.9204 (24.3732 ) 6.4883 (7.5817)
Oman -1.0224 ( 0.2573) -0.0656 (0.0226)
Qatar -0.8276 ( 0.2330) -0.0647 (0.0394)
0.0682 (0.0317) -0.2240 (0.2228) -0.0332 (0.0123) -0.632 (0.0264) 0.2058 (0.2346) 0.2915 (0.1692) 0.6703 (1.0234) -0.2269 (0.6620) -4.4277 (6.4880) 0.2317 (0.3232) 0.0773 (0.1123) -0.0446 (0.0315)
0.0195 (0.0330) -1.1383 (0.2632) 0.0181 (0.0644) 0.0509 (0.0359) 0.3314 (0.1239) -0.0201 (0.1029) 0.2678 (0.4427) -0.4107 (0.3976) 0.4893 (2.1676) -0.0839 (0.1457) -0.0067 (0.0174) -0.0311 (0.0633)
7.6913 (7.0575 )
11.8472 (15.0900)
Saudi Arabia -0.9322 ( 0.2689) -0.0914 (0.0174) 1.2679 (1.1958) -0.0858 (0.1052) -1.8895 (0.5915) 0.1786 (0.1630) 0.0587 (0.0674) -0.0939 (0.1841) 0.7851 (0.5836) -0.5091 (0.3479) -0.8049 (0.7263) -3.5392 (3.3478) 0.4394 (0.4374) 0.0092 (0.0097) -0.1409 (0.1134) 0.0400 (0.0691) 20.9303 (22.7363)
Table 2: Over Identifying Restrictions Test Countries Bahrain
Kuwait Oman Qatar
Saudi Arabia
210
Chi-Square | 2 (6) = 2.47
Significant level 0.82
| 2 (4) = 2.11
0.71
| 2 (6) = 1.69
0.94
| 2 (6) = 1.72 | (4) = 1.07 2
0.94 0.89
NEER
LR
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.06 0.06 0.08 0.25 0.03 0.05 0.05 0.06 0.05 0.05 0.06 0.06 0.11 0.31 0.41 0.28 0.02 0.02 0.03 0.02
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.44
0.30
0.18
0.14
0.17
0.07
0.03
0.02
0.04
0.04
0.03
0.02
0.01
0.01
0.00
0.00
0.13
0.04
0.01
0.00
FFR
0.02
0.01
0.01
0.00
0.16
0.08
0.03
0.00
0.04
0.03
0.02
0.02
0.09
0.05
0.03
0.03
0.41
0.63
0.82
0.91
GDP
0.08
0.04
0.03
0.01
0.14
0.13
0.10
0.08
0.02
0.02
0.02
0.02
0.72
0.82
0.87
0.89
0.02
0.04
0.04
0.02
CPI
M2
0.06
0.04
0.03
0.01
0.22
0.20
0.10
0.38
0.37
0.38
0.39
0.40
0.06
0.03
0.03
0.04
0.09
0.06
0.02
0.00
Table 3: Variance Decomposition: Basic SVAR Model-BA
0.21
0.30
0.44
0.54
0.13
0.21
0.38
0.49
0.26
0.26
0.27
0.27
0.05
0.03
0.01
0.00
0.07
0.10
0.03
0.01
LR
0.15
0.24
0.25
0.24
0.06
0.05
0.06
0.12
0.21
0.22
0.22
0.22
0.01
0.01
0.01
0.01
0.04
0.05
0.02
0.00
NEER
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212
NEER
MMR
M2
CPI
GDP
VARIANCE DECOMPOSITION
0.06 0.05 0.10 0.02 0.04 0.03
4
8
16
2
4
8
0.10
0.02
2
16
0.07
16
0.04
0.13
8
8
0.08
4
0.03
0.05
2
4
0.17
16
0.02
0.20
8
2
0.28
4
0.07
0.29
2
16
WOP
PERIOD
0.15
0.11
0.02
0.03
0.45
0.53
0.39
0.28
0.09
0.01
0.01
0.03
0.01
0.01
0.01
0.00
0.23
0.08
0.01
0.00
FFR
0.02
0.02
0.02
0.03
0.08
0.06
0.03
0.01
0.29
0.23
0.07
0.03
0.36
0.26
0.19
0.11
0.22
0.52
0.63
0.65
GDP
0.21
0.20
0.20
0.14
0.07
0.03
0.02
0.03
0.01
0.01
0.02
0.00
0.32
0.46
0.54
0.69
0.01
0.01
0.03
0.04
CPI
Table 4: Variance Decomposition: Basic VAR Model-KU
0.37
0.19
0.05
0.01
0.06
0.03
0.02
0.01
0.45
0.63
0.76
0.82
0.20
0.06
0.03
0.00
0.25
0.10
0.01
0.00
M2
0.03
0.08
0.11
0.14
0.26
0.29
0.39
0.48
0.07
0.03
0.02
0.02
0.03
0.05
0.10
0.10
0.07
0.05
0.04
0.01
MMR
0.12
0.36
0.57
0.65
0.01
0.03
0.11
0.17
0.02
0.04
0.06
0.08
0.01
0.02
0.05
0.05
0.05
0.04
0.03
0.01
ER Evaluating the Effects of Monetary Policy Shocks on GCC Countries
NEER
LR
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.15 0.12 0.07 0.04 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.07 0.06 0.04 0.04 0.12 0.12 0.10 0.05
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.16
0.06
0.07
0.09
0.31
0.33
0.08
0.00
0.13
0.17
0.03
0.00
0.07
0.06
0.00
0.00
0.02
0.00
0.00
0.00
FFR
0.13
0.08
0.05
0.04
0.13
0.15
0.03
0.00
0.24
0.07
0.01
0.03
0.28
0.21
0.18
0.09
0.76
0.80
0.81
0.82
GDP
0.36
0.06
0.00
0.00
0.30
0.12
0.02
0.01
0.04
0.03
0.05
0.05
0.51
0.62
0.76
0.88
0.01
0.01
0.00
0.00
CPI
Table 5: Variance Decomposition: Basic VAR Model-OM
0.03
0.01
0.01
0.02
0.10
0.10
0.24
0.27
0.06
0.18
0.41
0.59
0.03
0.01
0.01
0.01
0.02
0.01
0.00
0.00
M2
0.15
0.37
0.32
0.19
0.06
0.16
0.42
0.48
0.49
0.44
0.33
0.18
0.08
0.06
0.02
0.01
0.09
0.04
0.02
0.01
LR
0.13
0.32
0.43
0.54
0.06
0.10
0.15
0.18
0.03
0.09
0.15
0.14
0.02
0.02
0.03
0.01
0.07
0.08
0.05
0.01
NEER
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214
NEER
LR
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.08 0.08 0.05 0.03 0.02 0.01 0.03 0.09 0.00 0.00 0.01 0.11 0.09 0.04 0.04 0.08 0.02 0.02 0.04 0.02
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.09
0.01
0.00
0.01
0.23
0.40
0.44
0.33
0.07
0.16
0.13
0.04
0.06
0.07
0.02
0.01
0.08
0.03
0.01
0.00
FFR
0.04
0.04
0.01
0.00
0.37
0.19
0.05
0.01
0.02
0.03
0.02
0.00
0.01
0.01
0.01
0.02
0.53
0.70
0.84
0.91
GDP
0.30
0.17
0.09
0.06
0.19
0.19
0.13
0.03
0.11
0.12
0.03
0.00
0.58
0.73
0.89
0.93
0.03
0.00
0.00
0.00
CPI
Table 6: Variance Decomposition: Basic VAR Model-QA
0.14
0.11
0.05
0.02
0.02
0.02
0.01
0.00
0.33
0.57
0.80
0.94
0.05
0.04
0.02
0.01
0.19
0.11
0.02
0.00
M2
0.22
0.29
0.32
0.32
0.05
0.09
0.25
0.40
0.26
0.04
0.01
0.00
0.16
0.06
0.02
0.00
0.10
0.07
0.02
0.00
LR
0.19
0.34
0.51
0.66
0.05
0.05
0.08
0.14
0.10
0.08
0.01
0.00
0.07
0.05
0.02
0.01
0.06
0.04
0.03
0.01
NEER Evaluating the Effects of Monetary Policy Shocks on GCC Countries
NEER
TB
M2
CPI
GDP
VARIANCE DECOMPOSITION
WOP 0.41 0.43 0.29 0.14 0.01 0.01 0.01 0.03 0.01 0.01 0.02 0.01 0.04 0.02 0.02 0.12 0.03 0.02 0.06 0.09
PERIOD
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
2
4
8
16
0.21
0.06
0.03
0.03
0.53
0.79
0.85
0.74
0.10
0.02
0.01
0.01
0.07
0.04
0.04
0.02
0.24
0.10
0.04
0.01
FFR
0.05
0.03
0.01
0.01
0.08
0.02
0.03
0.05
0.08
0.04
0.01
0.01
0.04
0.05
0.08
0.08
0.08
0.19
0.30
0.55
GDP
0.07
0.03
0.02
0.00
0.06
0.03
0.01
0.00
0.07
0.12
0.12
0.09
0.67
0.73
0.79
0.86
0.08
0.08
0.03
0.00
CPI
Table 7: Variance Decomposition: Basic VAR Model-SA
0.05
0.03
0.02
0.01
0.07
0.05
0.01
0.02
0.51
0.65
0.78
0.83
0.08
0.05
0.02
0.01
0.31
0.22
0.09
0.01
M2
0.11
0.19
0.24
0.23
0.08
0.03
0.03
0.05
0.15
0.10
0.05
0.04
0.06
0.04
0.02
0.01
0.09
0.08
0.04
0.01
TB
0.42
0.59
0.67
0.68
0.05
0.06
0.05
0.10
0.08
0.05
0.02
0.01
0.09
0.07
0.04
0.01
0.06
0.04
0.04
0.01
NEER
Sayyed Mahdi Ziaei
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