Does the twin deficit hypothesis hold in the OECD countries under different real interest rate regimes?

Journal Pre-proof Does the Twin Deficit Hypothesis Hold in the OECD Countries under Different Real Interest Rate Regimes? ˘ Mustafa Erhan Bilman, Sadık Karaoglan

PII:

S0161-8938(19)30115-2

DOI:

https://doi.org/10.1016/j.jpolmod.2019.09.003

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JPO 6555

To appear in:

Journal of Policy Modeling

Received Date:

23 May 2019

Revised Date:

25 July 2019

Accepted Date:

30 August 2019

˘ Please cite this article as: Bilman ME, Karaoglan S, Does the Twin Deficit Hypothesis Hold in the OECD Countries under Different Real Interest Rate Regimes?, Journal of Policy Modeling (2019), doi: https://doi.org/10.1016/j.jpolmod.2019.09.003

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Does the Twin Deficit Hypothesis Hold in the OECD Countries under Different Real Interest Rate Regimes? Mustafa Erhan Bilmana Sadık Karaoğlanb a

Department of Economics, Izmir Katip Celebi University, Izmir, Turkey

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Department of Business, Izmir Katip Celebi University, Izmir, Turkey

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Corresponding author: Mustafa Erhan Bilman

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E-mail:

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Address: Department of Economics, Izmir Katip Celebi University, Izmir, Turkey

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Other authors: Sadık Karaoğlan

Time schedule of submission: 23 May 19

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Received:

25 July 19

Accepted:

30 Aug 19

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Revised:

Publication Type and Size: Full Length Article (FLA) Number of Pages:

14 1

Editorial Office Note:

Approved by: SC

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Date: 20 09 19

7 Dreve Lansrode, Rhode St. Genese, Belgium 1640

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E-mail: [email protected]

Abstract

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This paper explores the validity of the twin deficit hypothesis in selected 25 OECD countries with annual data for 2005-2016 by considering different real interest rate regimes. A nondynamic panel threshold model, introduced by Hansen (1999), is employed. The novelty of the empirical findings from the present study is that there exists a non-linear relationship between the budget deficit and the trade balance, which is driven by a critical threshold level in the real interest rates. The findings suggest that twin deficit hypothesis holds only under the low real interest rate regime, that is, rises in budget deficits lead to deteriorations in the trade balance when the real interest rate is below the threshold level. When the high real interest rate (i.e. above-the-threshold) regime is concerned, increasing budget deficits give rise to improvements in the trade balance, a finding consistent with the twin divergence hypothesis. Thus, the effect on the trade balance of an expansionary fiscal policy that worsens the budget balance reverses substantially depending on the threshold level of the real interest rates. The major policy implication of this paper is that the policy makers in the selected OECD countries should pay a greater attention to fiscal discipline in order to prevent the trade balance from worsening, because the majority of the countries fall into the low real interest rate regime over the recent years of the sample period (i.e. between 2010 and 2016). Keywords: Twin deficits, Twin divergence, Panel threshold model JEL classification: C33, E62, F32, H62

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1. Introduction

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The association between rising trade deficits and chronic fiscal deficits has been attracting the attention of policy makers, academics, and even the laypersons since the early 1980s when the two deficits grew dramatically in the developed nations including the U.S. Since the two deficits or the “twins” soared in the U.S. between 1980 and 1986, the earliest studies scrutinize the U.S. data (McKinnon, 1980; Laney, 1984; Summers, 1986; Gordon, 1986; Miller & Russek, 1989; McKinnon, 1990; Abell, 1990).

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Twin deficits can be linked by two theoretical approaches. First approach which considers the Mundell-Fleming framework assuming perfect capital mobility and flexible exchange rates is as follows: An increase in fiscal deficits brings about a rise in real domestic interest rates, which attracts foreign capital inflows and results in the appreciation of the domestic currency, giving rise to a deterioration in the trade balance due to increased imports and discouraged exports. This channel is also the case under fixed exchange rate regimes. The fiscal stimulus under fixed exchange rates generates higher real income that worsens the trade balance. The latter approach is the Keynesian absorption theory. According to this theory, an increase in the budget deficit motivates domestic absorption and thus increased imports, which in turn deteriorates the trade balance. Besides, depending on Feldstein & Horioka (1980), a strong correlation between national saving and domestic investment may support the twin deficit hypothesis (TDH henceforth). They argue that measures to increase national savings would be likely to lead to higher investment rates, and hence to economic growth. To the extent that the link from national savings to investments flows through interest rates, a greater fiscal deficit may lead to an increase in the trade deficit, to the extent that higher interest rates induce an appreciation of the domestic currency . Ricardian equivalence hypothesis (REH), initially introduced by Barro (1974; 1989) postulates in contrast with the TDH that an increase in the budget deficit will lead to an instantaneous equal increase in private savings, that is, long-lived agents are farsighted enough to predict the future taxes needed to finance current fiscal deficits. For this reason, they lower their consumption and investment spending, which eliminates the link between the fiscal and trade balance. 3

Kim & Roubini (2008) argue that co-movement of fiscal and trade deficits is not the typical feature of the entire U.S. data. Their findings suggest that government budget deficit shocks are associated with an improvement of the trade balance and a depreciation of the real exchange rate. This divergence between budget and trade deficits (i.e. the twin divergence hypothesis) may stem from the endogenous movements of the government budget and the trade account. They also put forward that that paradoxical correlation occurs even when they control for the effects of output shocks that are likely to generate a twin divergence. That is, even the exogenous fiscal deficit shocks are associated with an improvement of the trade balance.

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Though the TDH has already been widely investigated by time series or panel data analyses, the findings are inconclusive. A large number of earlier studies found evidence in favor of the TDH (see Summers, 1986; Darrat, 1988; Abell, 1990; Bachman, 1992; Zietiz & Pemberton, 1990; Kasa, 1994; Normandin, 1999; Piersanti, 2000; Cavallo, 2005; Salvatore, 2006, among others). There is also a vast body of empirical evidence supporting the REH (see Miller & Russek, 1989; Enders & Lee, 1990; Dewald & Ulan, 1990; Andersen, 1990; Kim, 1995; Kaufmann, Scharler, & Winckler, 2002, among others). Moreover, as stated above, Kim & Roubini (2008) found evidence in favor of a divergence instead of a co-movement between the two deficits.

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Recent studies revive the debate on the twin deficits by laying emphasis on the inconclusiveness of the empirical findings. According to Trachanas & Katrakilidis (2013), one of the main reasons for the mixed results is that the majority of the researches to model the TDH have followed a linear framework. Bagnai (2006), Daly & Siddiki (2009), Rafiq (2010), and Holmes (2011) also note that the relationship between the twin deficits changes substantially when non-linearity is permitted in the procedures. More recently, Ahmad, Aworinde, & Martin (2015) proved in a threshold cointegration framework that the relationships between the twin deficits in certain African countries may be non-linear depending on a threshold level in fiscal balance.

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By taking these criticisms into account, the present paper examines the TDH in a sample of OECD countries by considering that the relationship between the budget deficit and the trade balance may take a non-linear form contingent on a threshold level in the real interest rates. A panel threshold model is employed to explore the hypothesized non-linearity. To the best of our knowledge, such a panel threshold setting has never been adopted before by a previous study to investigate the validity of the TDH in the OECD countries. The rest of the paper is organized as follows. Second section describes the model and the data and outlines the empirical methodology. Empirical findings are discussed in the third section. And finally, the fourth section concludes and puts forward some major fiscal policy recommendations. 2. Model, Data, and the Methodology 2.1 Model and the Data

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The purpose of this paper is to investigate the relationship between the budget deficit and the trade balance in selected 25 OECD countries1 by using annual data for the period 2005-2016. By following Rosensweig & Tallman (1993) and Kim & Roubini (2008) the determinants for the trade balance are defined as follows: 𝑇𝐵𝑡 = 𝑓(𝑅𝐺𝐷𝑃𝑡 , 𝑅𝐸𝑋𝐶𝑡 , 𝐵𝐷𝑡 , 𝑅𝐼𝑁𝑇𝑡 )

(1)

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In equation (1), 𝑇𝐵𝑡 stands for the trade balance calculated as net trade divided by the gross domestic product (GDP), 𝐵𝐷𝑡 represents the general government deficit as a percentage of GDP, 𝑅𝐺𝐷𝑃𝑡 implies the real GDP per capita in local currency at 2010 prices, 𝑅𝐸𝑋𝐶𝑡 shows the real exchange rate index (2010= 100) where an increase implies an appreciation of the local currency, and 𝑅𝐼𝑁𝑇𝑡 is the real interest rate. In this basic model, 𝑅𝐺𝐷𝑃𝑡 controls for the broad economic activity whereas 𝑅𝐼𝑁𝑇𝑡 and 𝑅𝐸𝑋𝐶𝑡 control for the transmission channels for the fiscal policy through which it affects 𝑇𝐵𝑡 . The data is collected from the OECD online database. Descriptive statistics for the data is presented in Table A1in Appendix. 2.2 Methodology

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As stated above, the vast literature regarding the TDH can be characterized by the inconclusiveness of the empirical findings. This study is motivated by the fact that this inconclusiveness may arise from non-linear relationships between the twin deficits. This paper hypothesizes that a fiscal expansion or contraction may have differing effects on the trade balance depending on whether the real interest rates surpass a critical threshold level. For this reason, a non-dynamic panel threshold setting, introduced by Hansen (1999), is employed, which allows for non-linear relationships between the dependent variable and independent variables, which are determined by a threshold level, or more than one threshold levels of a specific variable.

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This setting depends on balanced panels. The reliability of this procedure for unbalanced panels is unknown. The model for a single threshold is as follows: 𝑦𝑖𝑡 = 𝜇𝑖 + 𝛽1 𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 ≤ 𝛾) + 𝛽2 𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 > 𝛾) + 𝑒𝑖𝑡

(2)

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In equation (2), subscripts 𝑖 and 𝑡 show the individual and time, respectively. 𝜇𝑖 is the constant. 𝑦𝑖𝑡 stands for the dependent variable, 𝑇𝐵𝑖𝑡 . 𝑞𝑖𝑡 is the threshold variable, 𝑅𝐼𝑁𝑇𝑖𝑡 , and 𝛾 is the threshold parameter. 𝑥𝑖𝑡 represents the regressors, 𝑅𝐺𝐷𝑃𝑖𝑡 , 𝑅𝐸𝑋𝐶𝑖𝑡 , 𝐵𝐷𝑖𝑡 , and 𝑅𝐼𝑁𝑇𝑖𝑡 , which are 𝑘 vectors, and 𝐼(. ) implies the indicator function. The above equation is split into two different regimes, the effects of which are represented by differing coefficients 𝛽1 and 𝛽2. The error 𝑒𝑖𝑡 is assumed to be independent and identically distributed (iid) with mean zero and finite variance 𝜎 2 . Ordinary least squares estimator is used to estimate 𝛽1 and 𝛽2 in equation (2) for any given 𝛾. 𝛽̂ (𝛾) = {𝑋 ∗ (𝛾)′ 𝑋 ∗ (𝛾)}−1 𝑋 ∗ (𝛾)′ 𝑌 ∗

(3)

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The following countries are selected depending on data availability: Australia, Austria, Belgium, Canada, Switzerland, Czech Republic, Germany, Denmark, Spain, Finland, France, United Kingdom, Greece, Ireland, Israel, Italy, Korea, Netherlands, Norway, New Zealand, Poland, Portugal, Sweden, United States, and South Africa.

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In equation (3), 𝑋 ∗ (𝛾) and 𝑌 ∗ are within-group deviations. The residual sum of squares (RSS) is 𝑆1 (𝛾), which is equal to 𝑒̂ ∗ (𝛾)′ 𝑒̂ ∗ (𝛾). The least squares estimator of 𝛾, which minimizes the RSS is as follows: 1 (𝛾) 𝛾̂ = 𝑎𝑟𝑔𝑚𝑖𝑛𝑆 𝛾

(4)

The model turns into an ordinary linear model, when 𝛾 is known. But if 𝛾 is unknown, there is a nuisance parameter problem, which makes the 𝛾 estimator’s distribution non-standard. Hansen (1999) proved that 𝛾̂ is a consistent estimator for 𝛾, and he argued that the best way to test 𝛾 = 𝛾0 is to form a confidence interval using the no-rejection region method with a likelihood-ratio (LR) statistic, as follows : {𝐿𝑅1 ( 𝛾) − 𝐿𝑅1 (𝛾̂)} 𝑃𝑟 → 𝜉 𝜎̂ 2

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𝐿𝑅1 ( 𝛾) =

−𝑥

Pr(𝑥 < 𝜉) = (1 − 𝑒 2 )2

(5)

(𝑆0 −𝑆1 ) ̂2 𝜎

(6)

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𝐹1 =

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Testing for a threshold is identical to testing to find out whether the coefficients are the same in the different regimes. The null of a linear model (i.e. no threshold effect) is tested against the alternative of a single threshold model. Namely, 𝐻0 : 𝛽1 = 𝛽2 and 𝐻𝑎 : 𝛽1 ≠ 𝛽2. F statistic is calculated as follows:

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The threshold parameter is unknown under the null hypothesis and 𝐹1 has non-standard asymptotic distribution that the critical values for the F statistic should be bootstrapped to test the statistical significance of the threshold effect. 𝑆0 is the RSS for the linear model.

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When a significant threshold (i.e. two different regimes) is (are) detected, the following double threshold model where three different regime coefficients exist should be tested. 𝑦𝑖𝑡 = 𝜇𝑖 + 𝛽1 𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 < 𝛾1 ) + 𝛽2 𝑥𝑖𝑡 𝐼(𝛾1 ≤ 𝑞𝑖𝑡 < 𝛾2 ) + 𝛽3 𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 ≥ 𝛾2 ) + 𝑒𝑖𝑡

(7)

𝑟 ̂ 𝑟 )} ̂)−𝑆 {𝑆1 (𝛾 1 2 (𝛾 2 2 ̂22 𝜎

(8)2

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𝐹2 =

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This time, the null of one threshold is tested against the alternative of two thresholds by the F statistic calculated as follows. Critical values for the F statistic are obtained by bootstrapping.

If the two threshold model is found to be valid, then the method should be extended in a straightforward manner to a higher-order threshold model (i.e. a three threshold model). 3. Empirical Findings Estimation and Inference

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The superscript r implies the refinement estimator which comes from the second-step estimation, i.e. the estimation of the double threshold model.

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To determine the number of thresholds, equations (2) and (7) are estimated by least squares allowing for zero, one, and two thresholds sequentially. A triple threshold model is also estimated. The test statistics 𝐹1 , 𝐹2 , and 𝐹3 , along with their bootstrap p-values, are shown in Table 13. According to Table 1, the F statistics for the single and double threshold models are of high statistical significance, whereas the F statistic for the triple threshold model is insignificant, with bootstrap p-values 0.0033, 0.0067, and 0.1267, respectively. Although these findings suggest that the double threshold model should be chosen, the regime coefficient of 𝐵𝐷𝑖𝑡 for 𝑅𝐼𝑁𝑇𝑖𝑡 > 1.359 and that of 𝑅𝐼𝑁𝑇𝑖𝑡 for 0.965 < 𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 1.359 are statistically insignificant in that model with p-values 0.581 and 0.115, respectively4. As regards the single threshold model, all the coefficients including those of the regime dependent 𝐵𝐷𝑖𝑡 and 𝑅𝐼𝑁𝑇𝑖𝑡 are highly significant (see Table 4). Hence, it is concluded that the single threshold (i.e. the two-regime) model illustrates best the relationship between the twin deficits.

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The threshold parameter 𝛾̂1, computed as 0.965 by the estimation of the single threshold model, and its 95% confidence interval are shown in Table 2. The asymptotic confidence interval for the threshold is very tight. Hansen (1999) puts that this is a sign of little uncertainty about the nature of the two different regimes.

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It is apparent from Figure 1 that the point estimate for the threshold parameter of the single threshold model, 𝛾̂1, i.e. 0.965 equalizes the LR statistic to zero by a sharp decline. Figure 1 constitutes a visual evidence supporting the absence of a second threshold effect, due to the reason that there is no other prominent decline in the LR statistic around the refined 𝛾̂2𝑟 , i.e. 1.359, which comes from the estimation of the double threshold model. Thus, the single threshold LR statistic does not reveal a second threshold in the regression.

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Table 3 reports the percentage of countries that fall into the two regimes each year. It is seen that the percentage of countries in the low real interest rate regime (i.e. 𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965 ) ranges from 4% to 84% whereas that of the high real interest rate countries (i.e. where 𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) ranges from 16% to 96% of the sample over the period 2005-2016.

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The findings from the estimation of the single threshold model are as follows: 𝑅𝐺𝐷𝑃𝑖𝑡 has a positive but trivial effect on 𝑇𝐵𝑖𝑡 . 𝑅𝐸𝑋𝐶𝑖𝑡 affects 𝑇𝐵𝑖𝑡 negatively, with a coefficient -0.080. In the low real interest rate regime, 𝐵𝐷𝑖𝑡 has a negative and substantial effect on 𝑇𝐵𝑖𝑡 with a regime coefficient -0.242. As for the high real interest rate regime, the relationship between 𝐵𝐷𝑖𝑡 and 𝑇𝐵𝑖𝑡 reverses in sign with a regime coefficient 0.128. As to the relationship between 𝑅𝐼𝑁𝑇𝑖𝑡 and 𝑇𝐵𝑖𝑡 , the sign is negative and positive in low and high real interest rate regimes, respectively with considerable regime coefficients -1.479 and 0.156. These findings reveal 3 4

Critical values are computed by 300 bootstrap replications. See Table A2 in Appendix.

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that the TDH holds in the selected OECD countries only under the low real interest rate regime, that is, 1% increase in 𝐵𝐷𝑖𝑡 leads to a 0.242% deterioration in 𝑇𝐵𝑖𝑡 when 𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965. When the high real interest rate regime (i.e. 𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) is concerned, 1% increase in fiscal deficits leads to a 0.128% improvement in 𝑇𝐵𝑖𝑡 , a finding consistent with the twin divergence hypothesis. What is also interesting about these findings is that the differing behavior of 𝐵𝐷𝑖𝑡 and 𝑅𝐼𝑁𝑇𝑖𝑡 in the two regimes is consistent with the theoretical expectations. That is, both 𝐵𝐷𝑖𝑡 and 𝑅𝐼𝑁𝑇𝑖𝑡 have a negative relationship with the 𝑇𝐵𝑖𝑡 in the low real interest rate regime, supporting the TDH.

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To sum up, we discovered that 𝐵𝐷𝑖𝑡 affects 𝑇𝐵𝑖𝑡 differently in low and high real interest rate regimes with considerable coefficients, a finding that brings up a novel fiscal policy implication. The policy makers in the selected OECD countries should take into consideration the level of the 𝑅𝐼𝑁𝑇𝑖𝑡 whilst designing a fiscal policy since that policy would have different effects on the 𝑇𝐵𝑖𝑡 depending on a critical threshold level in the 𝑅𝐼𝑁𝑇𝑖𝑡 . More specifically, a deepening fiscal deficit leads to twin deficits when the level of 𝑅𝐼𝑁𝑇𝑖𝑡 is below 0.965 while the same increasing deficit improves the 𝑇𝐵𝑖𝑡 when 𝑅𝐼𝑁𝑇𝑖𝑡 is above 0.965, leading to a divergence between the twins. Robustness Check

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The single threshold model selected in the present study is determined by the “general to specific” approach which serves as a robustness check. This approach entails the estimation of the broadest model possible, which allows for pairwise non-linear relationships between the independent variables and the dependent variable. The estimation process continues in a straightforward manner by making the model narrower if the estimation results from the earlier model so require5. The estimation results for the broadest model are presented in Table A3. Those results suggest that 𝑅𝐺𝐷𝑃𝑖𝑡 has positive but negligible effects on 𝑇𝐵𝑖𝑡 , and the negative coefficients of 𝑅𝐸𝑋𝐶𝑖𝑡 are almost identical in both regimes, that is, 𝑅𝐺𝐷𝑃𝑖𝑡 and 𝑅𝐸𝑋𝐶𝑖𝑡 have regime-independent coefficients. On the contrary, 𝐵𝐷𝑖𝑡 and 𝑅𝐼𝑁𝑇𝑖𝑡 have substantial coefficients which have negative and positive signs in low and high real interest rate regimes, respectively, that is, they have a non-linear relationship with the 𝑇𝐵𝑖𝑡 . In the next step, the model shown in Table A4 is estimated, where 𝑅𝐺𝐷𝑃𝑖𝑡 , the coefficient of which is positive and trivial, is considered independent from the regime, depending on the findings from the previous model in Table A3. 𝑅𝐸𝑋𝐶𝑖𝑡 has still the persisting negative coefficients in both regimes. The regime dependent coefficients of 𝐵𝐷𝑖𝑡 and 𝑅𝐼𝑁𝑇𝑖𝑡 also persist strongly that the nonlinearity remains. Finally, the model shown in Table A5 is estimated, where 𝑅𝐸𝑋𝐶𝑖𝑡 appears as a regime independent variable, the coefficient of which is still negative and somewhat the same. 𝑅𝐺𝐷𝑃𝑖𝑡 is excluded from the model due to its negligible effect on the 𝑇𝐵𝑖𝑡 . The coefficients of the regime dependent 𝐵𝐷𝑖𝑡 and 𝑅𝐼𝑁𝑇𝑖𝑡 differ only very slightly from those estimated in the previous models for robustness check. According to the findings from

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The models estimated for robustness check are presented in Table A3, Table A4, and Table A5 in Appendix.

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the models for robustness check, it is concluded that the coefficient estimates of the selected single threshold model are robust. 4. Conclusion and Policy Recommendations

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Exploring the relationship between the fiscal deficits and the trade balance is of vital importance due to the reason that such an investigation is equivalent to determining the appropriate fiscal policy which may have a potential to affect the external balance of an economy. There is a large body of empirical researches concerning the effects of a fiscal deficit on the trade balance, the findings of which are inconclusive. Some of the earlier studies found evidence that increasing fiscal deficits give rise to the worsening of the trade balance, considering the two deficits twins, i.e. the twin deficit hypothesis (TDH), while some others found empirical support in favor of the hypothesis that there is no link between the two deficits, i.e. the Ricardian equivalence theory. Finally, more recently Kim & Roubini (2008) argued that an improving trade balance may be associated with enlarging fiscal deficits, leading the twins to diverge from each other, i.e. the twin divergence hypothesis. This study is motivated by the fact that the mixed empirical findings in the literature may result from the existence of non-linear relationships between the budget deficit and the trade balance.

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This paper investigates the validity of the TDH in selected OECD countries by using annual data over the period 2005-2016. It is hypothesized in this paper that the non-linearity in the relationship between the fiscal deficit and the trade balance is driven by a critical threshold level (or more than one threshold levels) in the real interest rates. A panel threshold procedure, introduced by Hansen (1999), is employed, which allows for non-linear relationships between the dependent variable and independent variables, which are contingent on the different regimes of a particular variable. As far as the authors of the present paper are concerned, the panel threshold setting depicted here has never been utilized before by any previous study to discover the relationship between the twin deficits in the OECD countries.

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The empirical findings suggest that the non-linear relationship between the fiscal deficit and the trade balance is led by a statistically significant threshold parameter, 0.965, in the real interest rates. According to the estimation results from the single threshold model, TDH holds only in the low real interest rate regime, i.e. when 𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965. When the high real interest rate regime is concerned, i.e. 𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965, the twin divergence phenomenon, instead of a co-movement between the two deficits, is the case. These findings propose a serious fiscal policy implication: An expansionary fiscal policy which leads to deepening budget deficits can be used as an instrument to ameliorate the trade balance only when the economy is in the high real interest rate regime. The consequence of the same easy fiscal policy would be the worsening of the trade balance when the low real interest rate regime is concerned. This study concludes that the policy makers in the selected OECD countries should take the fiscal discipline more seriously in order to maintain a balanced trade, since the percentage of these countries that fall into the “twin-deficit-generating” low real interest rate regime ranges from 52% to 84% between 2010 and 2016, which is the recent portion of the sample period (see Table 3).

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Bachman, D. D. (1992). Why Is the US Current Account Deficit So Large? Evidence from Vector Autoregressions. Southern Economic Journal, 59, 232-240.

Bagnai, A. (2006). Structural Breaks and the Twin Deficits Hypothesis. International Economics and Economic Policy, 3, 137-155.

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Barro, R. J. (1974). Are Government Bonds Net Wealth? Journal of Political Economy, 82(6), 10951117.

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.5 1 1.5 2 Threshold Parameter

2.5

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Figure 1. LR statistic of the single threshold model

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Table 1. Threshold effect tests Single threshold model

1% 31.5411

10% 21.0497

5% 26.6887

1% 31.6269

10% 31.2317

5% -

1% -

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57.84 𝐹1 p-value 0.0033 Double threshold model 36.37 𝐹2 p-value 0.0067 Triple threshold model 21.23 𝐹3 p-value 0.1267

Critical values 10% 5% 19.4685 23.6384

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Table 2. Threshold parameter estimate Models

Threshold parameter

Point estimate

95% confidence interval

𝛾̂1

0.9650

[0.9300, 0.9660]

Double threshold

𝛾̂1𝑟

0.9650

[0.9300, 0.9720]

𝛾̂2𝑟

1.3590

[1.3065, 1.3810]

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Single threshold

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Table 3. Percentage of countries in each regime by year 𝑹𝑰𝑵𝑻𝒊𝒕 ≤ 𝟎. 𝟗𝟔𝟓 20 12 4 12 12 76 84 68 68 52 52 72

𝑹𝑰𝑵𝑻𝒊𝒕 > 0.965 80 88 96 88 88 24 16 32 32 48 48 28

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Year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

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Table 4. Regression estimates of the single threshold model Coefficients 0.000001 -0.08014 -0.24209 0.12768 -1.47901 0.15560 9.18042

OLS SE 0.0000 0.0199 0.0591 0.0469 0.3349 0.0488 1.9864

p-values 0.000 0.000 0.000 0.007 0.000 0.002 0.000

Robust SE 0.0000 0.0324 0.0714 0.0730 0.2959 0.0885 3.0954

Robust p-values 0.000 0.021 0.002 0.093 0.000 0.091 0.007

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Independent variables 𝑅𝐺𝐷𝑃𝑖𝑡 𝑅𝐸𝑋𝐶𝑖𝑡 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) c

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Appendix Table A1. Descriptive statistics Minimum -14 -32.03 -3.88 72.01 16028.16

25% quantile -1.10 -4.23 0.68 96.33 31495.90

Median 2.05 -2.55 1.04 100 40975.59

75% quantile 5.55 0.18 1.54 102.37 79367.46

Maximum 22.90 18.67 41.07 152.99 29500000

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Variables 𝑇𝐵𝑖𝑡 𝐵𝐷𝑖𝑡 𝑅𝐼𝑁𝑇𝑖𝑡 𝑅𝐸𝑋𝐶𝑖𝑡 𝑅𝐺𝐷𝑃𝑖𝑡

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Table A2. Regression estimates of the double threshold model Coefficients 0.00000107 -0.07918 -0.19927 0.41638 0.02667 -1.34627 0.49003 0.09847 9.15706

OLS SE 0.0000 0.0189 0.0568 0.0677 0.0483 0.3309 0.3098 0.0478 1.9019

p-values 0.000 0.000 0.001 0.000 0.581 0.000 0.115 0.040 0.000

Robust SE 0.0000 0.0305 0.0726 0.0647 0.0627 0.3062 0.3091 0.0512 2.9061

Robust p-values 0.000 0.016 0.011 0.000 0.675 0.000 0.126 0.067 0.004

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Independent variables 𝑅𝐺𝐷𝑃𝑖𝑡 𝑅𝐸𝑋𝐶𝑖𝑡 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝐵𝐷𝑖𝑡 𝐼(0.965 < 𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 1.359) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 1.359) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(0.965 < 𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 1.359) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 1.359) c

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Table A3. Robustness check I for the single threshold model Independent variables

p-values

Robust SE

Robust p-values

0.000 0.000 0.000 0.000 0.000 0.007 0.002 0.004 0.000

0.0000 0.0000 0.0401 0.0368 0.0886 0.0725 0.2290 0.0849 3.5936

0.000 0.000 0.044 0.035 0.007 0.092 0.000 0.095 0.015

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𝑅𝐺𝐷𝑃𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐺𝐷𝑃𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝑅𝐸𝑋𝐶𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐸𝑋𝐶𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) c

Coefficients OLS SE 0.00000107 0.0000 0.00000109 0.0000 -0.08540 0.0217 -0.08231 0.0210 -0.26081 0.0659 0.12706 0.0471 -1.30314 0.4138 0.14768 0.0502 9.46269 2.0956

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Table A4. Robustness check II for the single threshold model Independent variables

p-values 0.000 0.000 0.000 0.000 0.007 0.002 0.003 0.000

Robust SE 0.0000 0.0377 0.0340 0.0893 0.0718 0.2254 0.0847 3.3354

Robust p-values 0.000 0.035 0.025 0.007 0.089 0.000 0.094 0.010

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𝑅𝐺𝐷𝑃𝑖𝑡 𝑅𝐸𝑋𝐶𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) REXCit 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) c

Coefficients OLS SE 0.000001 0.0000 -0.08453 0.0209 -0.08135 0.0200 -0.26205 0.0653 0.12734 0.0469 -1.30588 0.4126 0.14773 0.0500 9.3721 2.0059

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Table A5. Robustness check III for the single threshold model Independent variables

p-values 0.001 0.000 0.010 0.000 0.002 0.000

Robust SE 0.0331 0.0700 0.0730 0.2984 0.0867 3.2714

Robust p-values 0.052 0.002 0.101 0.000 0.086 0.010

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𝑅𝐸𝑋𝐶𝑖𝑡 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝐵𝐷𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 ≤ 0.965) 𝑅𝐼𝑁𝑇𝑖𝑡 𝐼(𝑅𝐼𝑁𝑇𝑖𝑡 > 0.965) c

Coefficients OLS SE -0.06759 0.0202 -0.24388 0.0607 0.12470 0.0482 -1.43293 0.3442 0.15509 0.0501 9.12693 2.0541

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