Trade openness and economic growth in Turkey: A rolling frequency domain analysis

Çevik, Emrah İ.; Atukeren, Erdal; Korkmaz, Turhan Article Trade openness and economic growth in Turkey: A rolling frequency domain analysis Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Çevik, Emrah İ.; Atukeren, Erdal; Korkmaz, Turhan (2019) : Trade openness and economic growth in Turkey: A rolling frequency domain analysis, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 7, Iss. 2, pp. 1-16, https://doi.org/10.3390/economies7020041 This Version is available at: https://hdl.handle.net/10419/256973 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Çevik 1, Erdal Atukeren 2,3,4,* and Turhan Korkmaz 5 1Department of Economics, Namık Kemal University, Tekirda˘g 59030, Turkey; [email protected] 2BSL Business School Lausanne, Rte. de la Maladierè21, P.O. Box, CH-1022 Chavannes (VD), Switzerland 3SBS Swiss Business School, Flughafenstrasse 3, CH-8302 Kloten-Zürich, Switzerland 4School of Business, University of Applied Sciences and Arts Northwestern Switzerland (FHNW), CH-4600 Olten, Switzerland 5 Department of Business Administration, Mersin University, Mersin 33342, Turkey; [email protected] *Correspondence: er[email protected] or [email protected]; Tel.: +41-21-619-0606 Received: 6 March 2019; Accepted: 16 April 2019; Published: 8 May 2019   Abstract: Taking Turkey’s experience as a case study, this study provides further insights into the evaluation of time-varying Granger-causal relationships in the trade openness and economic performance nexus. We reinvestigated the Granger-causal relationships between trade openness and real economic growth in Turkey for the time period 1950–2014. We employed a rolling version of Breitung and Candelon’s frequency domain Granger-causality test, which allowed us to identify the changes in the nature of the causal relationships overtime. Hence, in the face of different results found in the literature overtime, our study provides a more unified evidence on the relationship between trade openness and real economic growth in Turkey. In addition, we found empirical evidence for the possibility of a distinct temporal ordering in a feedback relationship between trade openness and economic growth. We called this situation “sequential feedback”. Keywords: openness and economic growth; Granger-causality; frequency domain time series analysis; Turkey JEL Classification: C32; F14; F43; O47 1. Introduction Most developing economies undertook economic liberalization measures and implemented policies that increased their trade openness since the early-1980s. This was initiated partly by the debt crisis of the 1980s in developing countries and partly as a lesson from the examples of some of the Asian economies that pursued outward-oriented policies and achieved higher levels of real GDP growth. Hence, the relationship between economic growth and trade openness attracted the attention of policy makers and academicians. The economic liberalization policies undertaken in many developing countries in the 1980s also included a financial liberalization dimension. However, Tornell et al. (2003) indicate that developing economies generally have started liberalization policies with trade liberalization first and then financial liberalization typically followed. While the measurement issues on outward-oriented economic growth has its own literature e.g., Edwards (1992), trade openness that is usually taken as the ratio of the sum of exports and imports to GDP is used as a practical measure since it can serve as a proxy capture the ease of exchanging goods and services, capital, labor, information, and ideas across borders. In a recent review, Gräbner et al. (2018) , for instance, classified trade openness as a de facto measure of outward-orientation. It is also important Economies 2019,7, 41; doi:10.3390/economies7020041 www.mdpi.com/journal/economies Economies 2019,7, 41 2 of 16 to note that higher levels of trade openness correlate with higher levels of international financial markets integration. Economic theory suggests several channels through which an increase in trade openness might lead to higher economic growth rates. First, export revenues provide an important source of foreign exchange, which is crucial when domestic savings are inadequate for making imports of capital goods possible. Secondly, export growth might also trigger economic growth through the expansion of the efficient market size, bringing in substantial economies of scale that accelerate the rate of capital formation and technical change. Thirdly, outward-oriented policies are hypothesized to provide higher economic growth because they might increase overall productivity and efficiency in an economy due to productivity spillovers that result from the importation of more advanced technologies or by attracting foreign direct investment. Hye (2012) summarized the latter factors as the efficient allocation of scarce resources, technology spillover effects from developed countries to developing countries, and learning by doing effect that indicates a relationship between innovation and imitation. Economic theory emphasizes two hypotheses, namely, trade-led growth (or, more commonly, export-led growth) and growth-led trade (or, more commonly, domestically-generated exports), to understand the relationship between trade openness and economic growth. The trade-led growth hypothesis indicates that trade openness affects economic growth by stimulating total factor productivity through adopting advance technology and know-how from the developed countries. Hence, the hypothesis suggests the presence of a causal link running from trade openness to economic growth. The growth-led trade hypothesis is based on neoclassical trade theory which indicates that economic growth leads to productivity growth which in turn leads to an increase in the international competitiveness of export products. Hence, this hypothesis suggests that the causal relationship runs from economic growth to trade growth. On the other hand, empirical studies also showed that bidirectional causality relation might exist between trade openness and economic growth. Nevertheless, Chang et al. (2009) argued that the relationship between trade openness and economic growth is not stable over the countries and structural characteristics of a country can significantly affect the nature of the causal relationships. Shayanewako (2018, p. 4) also states that “[i]t is evident from the existing literature that there is no consensus on the trade-growth link and the results are mixed across countries, data and empirical techniques.” Given this background, our study particularly examines whether there exist changes in the (Granger-) causal dynamics between trade-openness and real economic growth in the case of Turkey. An in-depth understanding of the (Granger-) causal relationships between trade openness and real GDP growth in Turkey is not only important for Turkey but also for other developing countries since Turkey followed an import-substitution strategy until 1980 and switched to an outward-oriented strategy thereafter. Hence, an investigation of how the relationship between trade openness and real economic growth evolved in Turkey overtime (that is, over two opposing trade-orientation regimes) sheds further insights into the understanding of the relationship between trade openness and economic growth in general. The economic liberalization policies in Turkey started with the liberalization of the foreign trade regime in 1980 (known as the 24th January 1980 decisions) and the financial liberalization was completed in 1989 by removing all price controls. During these periods, all government subsidies and tariffs were reduced, the tax system was revised and the government started to conduct trade policies that stimulate exports. Against this background, the aim of the paper is to examine the existence (or lack thereof) of Granger-causal relationship between economic growth and trade openness by means of Breitung and Candelon’s (2006) frequency domain causality test for the 1950–2014 periods. In view of the argument by Chang et al. (2009) that the causal relationships (if any) between trade-openness and economic growth might change overtime as a country’s economic structure changes, we employ a rolling window version of Breitung and Candelon (2006) causality test to detect any time-varying characteristics of the causal relationships. This approach is also superior to using time dummy variables as they might lead to pre-test biases (Li et al. 2016). Given that a number of studies conducted overtime found different results on the trade openness and economic growth Economies 2019,7, 41 3 of 16 relationship in Turkey, our paper has the ability to provide a more unifying framework as we directly address and assess the time-varying nature of the causal relationships. As a second contribution to the literature, we detected evidence for the theoretical possibility of a distinct temporal ordering of variables in a causal feedback relationship, we call this case as a “sequential Granger-causal feedback”. The rest of paper is organized as follows: Section 2reviews the literature on the causality relation between economic growth and trade openness. In Section 3, we discuss the methodology employed in our paper. Section 4presents the data characteristics and the results from causality analysis. Finally, Section 5concludes. 2. Literature Review The studies that have examined the relationships between trade openness and economic growth in the empirical literature can be classified into three groups. The first group has generally considered individual aggregate country data; another group of studies employed more recent panel data analyses; a further line of studies focused on industry-level effects. Giles and Williams (2000a,2000b) provided a review of the literature until 2000. In what follows, we mainly review some of the studies on developing countries in the more recent periods. Among studies on developing countries that used aggregate individual country-level data that examined the causal relationships between trade openness and economic performance, Awokuse (2008) emphasized that imports are important as well as exports when analyzing the relation between trade and economic growth. Therefore, he examined the relation between imports, exports and economic growth by means of Granger-causality tests and impulse-response functions in Argentina, Colombia, and Peru. Empirical results suggested that the causal link running from imports to real economic growth is stronger than the Granger-causal relationship running from exports to real economic growth. Moreover, Awokuse (2008) found evidence for the reverse causality relationship from economic growth to exports and imports; hence leading to a Granger-causal feedback between exports and economic growth. Klasra (2011) investigated the presence of long-run relationships between the determinants of economic growth for Pakistan and Turkey by employing the autoregressive distributed lags (ARDL) model. Empirical results suggested the existence of bidirectional causal relationship between trade openness economic growth for Pakistan and foreign direct investment and exports for Turkey in the short-run. Klasra (2011) also found that economic growth Granger-caused exports in Turkey and trade openness Granger-caused real economic growth in Pakistan in the long-run. Shahbaz (2012) analyzed the relationship between trade openness and economic growth by using a Cobb-Douglas production function for Pakistan. Shahbaz employed four different types of proxies for trade openness. The proxies used were exports, imports, terms of trade, and trade per capita (i.e., the sum of real exports per capita and real imports per capita). Shahbaz’s empirical results supported the growth-led-exports, growth-led-imports, and growth-led-trade hypotheses for Pakistan. Bojanic (2012) found that financial development and financial development are the Granger cause of economic growth for Bolivia during the periods of 1940–2010. Hye and Lau (2015) empirically examined the relation between trade openness and economic growth in India by using the ARDL model and rolling-window regression approach. They showed that the effect of trade openness on economic growth was time-varying over the sample period and that trade openness affected real economic growth positively in the short run. However, economic growth was negatively affected by trade openness in the long run, which corresponds in the literature to the growth-reducing exports (or openness) hypothesis. The second group of studies examined the relation between trade openness and economic growth by employing cross-country or panel data. Accordingly, Gries et al. (2011) investigated causal link between finance, growth and trade openness for 13 Latin American and Caribbean countries. Their empirical results suggest that financial and trade openness are not preconditions of economic growth for the countries in questions. They also indicated that more balanced policy approach that also considers other fundamental factors of economic growth is more suitable for these countries. ( Kim et al. 2012 ) investigated the presence of Granger-causal relationships between Economies 2019,7, 41 4 of 16 economic growth, financial development, and trade openness by using a simultaneous equation model for 63 countries. Kim et al. (2012) empirical findings showed that trade openness stimulates economic growth in high-income, low-inflation, and non-agricultural countries. Although the effects of financial development on trade were positive, financial development was negatively affected by higher trade openness in countries with low-income, with high-inflation, or with larger agricultural sectors. Tekin (2012) examined the presence of Granger-causal relationships among the real GDP, real exports and real net foreign direct investment (FDI) inflows for the least developed countries for the period between 1970 and 2009 by means of panel causality test suggested by Kónya (2006) . Hossain and Mitra (2013) analyzed the presence of Granger-causal relationships between trade openness, foreign aid, domestic investment, external debt, government spending, and economic growth in 33 highly aid-dependent African countries for the periods of 1974–2009. Although bidirectional causal links between trade openness and economic growth were found in the short run, an increase in the trade openness triggered economic growth in the long run. Menyah et al. (2014) analyzed the Granger-causal relationships between the level of financial development, trade openness, and economic growth for 21 Sub-Saharan African countries by using panel Granger-causality tests. They found that financial development is a Granger-cause of economic growth only in three countries and the causal link between financial development and trade openness was limited. Overall, empirical results suggest that financial development and trade openness do not Granger-cause real economic growth for Sub-Saharan Africa. Sakyi et al. (2015) analyzed the relationship between trade openness and income levels in 115 developing countries for the period 1970–2009 using non-stationary heterogeneous panel cointegration tests. Their findings indicated the presence of a bi-directional or a feedback relationship both in the shortand in the long-run. The third line of studies in the trade openness and economic growth literature employs industry or sector-level analyses. Some examples of this approach for developing countries are Ghatak et al. (1997) for Malaysia; Parida and Sahoo (2007) for South Asian countries; Sahoo et al. (2014) for India; Toyin (2016) for South Africa; and Aslan and Topcu (2018) for Turkey. Despite different time period covered and different countries involved in the analyses, the outcome of the sectoral-level studies is no different than the aggregate country-level studies: that is, there is no clear Granger-causal relationship between exports or trade openness and real economic performance in developing countries. There is also a growing literature that focus on Granger-causal relationships between trade and economic growth in Turkey. Hatemi-J and Irandoust (2000) employed vector autoregressions (VARs) to examine the relationship between exports and economic growth in Turkey, Greece, Mexico, Ireland, and Portugal. Their findings on Turkey indicated no Granger-causal relationships between exports and economic growth. Yanikkaya (2003) explored the relation between economic growth and trade openness for Turkey. He considered two types of openness measures that include trade volumes and intensity. Yanikkaya’s results showed that trade promotes economic growth through a number of channels such as technology transfers, scale economies, and by creating comparative advantage. Utkulu and Kahyao˘glu (2005) examined the effect of trade and financial openness on economic growth in Turkey by means of threshold autoregressive (TAR) models and Markov regime-switching models. Their empirical findings indicated that increases in financial openness cause lead to a higher probability of remaining in a recession. Conversely, trade openness has a positive impact on economic growth in Turkey. On the other hand, Yapraklı (2007) found that the relationship between trade and financial openness and economic growth has different effects in the shortand long-run in Turkey. Whereas increases in trade openness and financial openness stimulate economic growth in the long run, there is evidence in favor of a bidirectional causality (feedback) relationship in the short run. Korkmaz et al. (2010) analyzed the effect of financial openness on economic growth and financial crisis simultaneously in Turkey and concluded that financial openness increased both economic growth and likelihood of a financial crisis. On the other hand, the effect of trade openness on economic growth is higher than its effect on the likelihood of a financial crisis. Kıran and Güri¸s (2011) analyzed the relation among trade and finance openness and economic growth by using the Bond Economies 2019,7, 41 5 of 16 test and the Toda-Yamamoto causality test in Turkey. They found evidence in favor of a bidirectional Granger-causal relationship between trade openness and economic growth. They also found that financial openness did not Granger-cause economic growth. Kar et al. (2014) analyzed the relation among trade liberalization, financial development and economic growth in Turkey for the periods of 1989–2007 by using both linear and nonlinear Granger-causality tests. Their empirical results suggested the presence of bidirectional causal relation among trade, financial development, and economic growth in Turkey. Çeliköz et al. (2017) used cointegration tests and a vector error correction model (VECM) to examine the Granger-causal relationships between trade openness and economic growth in Turkey for the 1980–2016 period. Çeliköz et al. (2017) detected unidirectional Granger-causality from trade openness to economic growth in the short-run. On the other hand, Eren and Ünal (2019) examined the Granger-causal relationships between trade openness and economic growth in Turkey for a longer time period—from 1960 to 2016—using Engle-Granger and Gregory-Hansen cointegration tests and Toda-Yamamoto Granger causality tests. Their findings showed no evidence in favor of a long-run relationship but indicated unidirectional causality from economic growth to trade openness. At the disaggregate level, there is some evidence that—at least in the post-1980 period—there might be a positive Granger-causal relationship from exports originating from the manufacturing sectors to real economic growth in Turkey. Aslan and Topcu (2018) reviews the evidence at the disaggregate level; see the analyses by Aslan and Topcu (2018, Table 3), which indicate similar findings. One interesting line of literature examining the statistical relationships between (trade-) openness and economic growth includes the role of the foreign direct investment (FDI) variable. The question is to what extent (trade-) openness already stands as proxy for a more favorable foreign investment environment that might enhance economic growth as well; or whether FDI has a separate additional causal effect on economic growth dynamics. In the context of Turkey and this paper, the general conclusion from the findings in the literature is that the FDI variable does not significantly enter the relationship when trade-openness is included. Karı¸s and Ayla (2018), for instance, used cointegration and Granger-causality tests and find a unidirectional Granger-causal link from trade openness to foreign direct investment in Turkey for the 1980–2016 period. Karı¸s and Ayla (2018, p. 256) conclude that “[t]rade openness acts as a stimulant in terms of foreign direct investment flows.” Erki¸si (2018) used quarterly data from 1998Q1 to 2018Q1 to examine the contributions of exports, imports, and foreign direct investment to real GDP growth in Turkey. The variance decomposition analyses indicate that real GDP growth is mostly explained by its own history (75%). Imports have a 20% contribution and the contributions of exports and foreign direct investment are 5% each. These findings also indicate that trade openness overall has a much larger effect on real GDP growth than foreign direct investment. Overall, it can be said that trade-openness serves as a (de facto) proxy for other variables that are associated with outward-orientation; hence FDI does not have much significant individual explanatory power on economic growth. 3. Econometric Methodology After the seminal paper of Granger (1969), a large number of studies have empirically analyzed the causal relationships between various economic and financial time series. Several improvements in testing methodology have been made in the literature. For instance, Granger (1988) proposed a cointegration test for nonstationary time series in which causal relationships can be examined by using an error correction model (ECM). Toda and Yamamoto (1995) suggested a test procedure based on an augmented-VAR model to examine the (Granger-) causal relationships between variables. A further recent development on the Granger-causality test has been its extension by Breitung and Candelon (2006) in the frequency domain. Note that Granger’s (1969) original analysis also used the frequency domain. The frequency-based decomposition of spectral density is based on Geweke (1982) and Hosoya (1991), and employs a Wald-type testing procedure for detecting causality at given frequencies. Nevertheless, this testing procedure has some drawbacks because testing for causality entails a complicated set of nonlinear restrictions on the autoregressive parameters. Economies 2019,7, 41 6 of 16 To overcome these issues, Yao and Hosoya (2000) proposed a delta method based on numerical derivatives in the frequency-based causality test. Recently, Breitung and Candelon (2006) suggested a test procedure based on frequency-domain causality measure by using a bivariate vector autoregressive (VAR) model and they showed that the test procedure is superior other frequency-domain approaches. In addition, Breitung and Candelon (2006) showed that the test procedure could be generalized to allow for cointegrating relationships and higher-dimensional systems. Technically speaking, the testing procedure by Geweke (1982), Yao and Hosoya (2000), and Hosoya (2001) can be outlined as follows. Let ht=[xt,yt]0 be a two-dimensional vector of time series observed at t=1, . . . , T where y t and x t are economic growth and trade openness respectively. It is assumed that h t has a finite-order VAR representation of the form: Θ(L)ht=εt(1) where Θ(L)=I−Θ1L− · · · − ΘpLp is a 2 × 2 lag polynomial with Lmht=ht−m . It is assumed that the error vector εt is white noise with E(εt)= 0 and E(εtεt0)=Σ , where Σ is positive definite. Let Gbe the lower triangular matrix of the Cholesky decomposition G0G=Σ−1 such that E(ηtηt0)=I and ηt=Gεt . If the system is assumed to be stationary, the MA representation of the system can be formulated as following: kt=Φ(L)εt="Φ11(L)Φ12(L) Φ21(L)Φ22(L)#" ε1t ε2t# =Ψ(L)ηt="Ψ11(L)Ψ12(L) Ψ21(L)Ψ22(L)#" η1t η2t#(2) where Φ(L)=Θ(L)−1 and Ψ(L)=Φ(L)G−1 . Using this representation, the spectral density of x t can be expressed as: fx(ω)=1 2π Ψ11e−iω 2+ Ψ12e−iω 2(3) Geweke (1982) and Hosoya (2001) defined the measure of causality as the following: My→x(ω)=log"2πfx(ω) |Ψ11(e−iω)|2# =log"1+Ψ12(e−iω) 2 |Ψ11(e−iω)|2#(4) If  Ψ12e−iω= 0, it can be said that ydoes not Granger cause xat frequency ω . Note that Yao and Hosoya (2000) suggested to estimate My→x(ω) by replacing  Ψ11e−iω and  Ψ12e−iω with estimates obtained from the fitted VAR model and then the delta method can be applied for testing the null hypothesis. On the other hand, it is based on complicated nonlinear restrictions in the VAR parameters and hence this testing procedure is very difficult. The methodology by Breitung and Candelon (2006), on the other hand, proposed a much simpler approach to test the null hypothesis, namely, that ydoes not Granger-cause xat frequency ω: My→x(ω)=0 UsingΨ(L)=Θ(L)−1G−1andΨ12(L)=−g22Θ12(L) |Θ(L)| (5) where g 22 is the lower diagonal element of G −1 and Θ(L) is the determinant of Θ(L) . It follows that y does not Granger-cause xat frequency ωif:  Θ12e−iω= p X k=1 θ12,kcos(kω)− p X k=1 θ12,ksin(kω)i =0 (6) Economies 2019,7, 41 7 of 16 where θ12,k is the (1,2)-element of Θk . Thus, a necessary and sufficient set of conditions can be written as following:  Θ12e−iω=0 is: p X k=1 θ12,kcos(kω)=0 (7) p X k=1 θ12,ksin(kω)=0 (8) The approach is based on the linear restrictions above equations. To simplify the notation, we let αj=θ11,jand βj=θ12,jso that the VAR equation for xtis written as: xt=α1xt−1+· · · +αpxt−p+β1yt−1+· · · +βpyt−p+ε1t(9) The hypothesis My→x(ω)=0 is equivalent to linear restriction: H0=R(ω)β=0 (10) where β=hβ1,. . . ,βpi0and: R(ω)="cos(ω) sin(ω) cos(2ω) sin(2ω) · · · · · · cos(pω) sin(pω)#(11) In order to the test the null of no causality, the ordinary F statistic, which is approximately distributed as F(2, T-2p), for ω∈(0,π) can be calculated. It should be noted that the testing methodology can be extended for the higher-dimensional systems. For instance, if we consider the effect of third (or common) variable on the causality relation between economic growth and trade openness, the causality relation in a three dimensional system can be formulated as follows:  yt xt zt  = Ψ11(L)Ψ12(L)Ψ13(L) Ψ21(L)Ψ22(L)Ψ23(L) Ψ31(L)Ψ32(L)Ψ33(L)   η1t η2t η3t  (12) where z t is third variable such as capital, labor, government expenditure, and terms of trade. Hosoya (2001) suggested that the causality measure is identical to the bivariate causality measure between utand vtas follows: My→x|z(ω)≡Mu→v(ω)(13) where ut=Ψ11(L)η1t+Ψ12(L)η2t and vt=Ψ21(L)η1t+Ψ22(L)η2t . Hence, the testing procedure for higher-dimensional system can be formulated as a bivariate causality measure with appropriately transformed variables. 4. Data and Estimation Results The aim of this study was to investigate the causality relation between economic growth and trade openness by means of rolling window frequency domain test proposed by Breitung and Candelon (2006) . We used annual data that were obtained from Feenstra et al. (2015): Penn World Tables (PWT) 9.0 covering the period from 1950 to 2014. The Penn World Tables data have the advantage of being calculated on time-wise and country-wise consistent definitions. We took the real GDP variable (at 2011 prices) to represent real economic growth and the sum of exports and imports to GDP ratio as a proxy for trade openness. Despite the fact that we tested for the bivariate Granger-causal relationships between trade openness and economic growth, we introduced additional control variables such as capital, labor, government expenditure, and terms of trade developments into the test equations. Hence, while we examined the bivariate frequency-domain Granger-causal Economies 2019,7, 41 8 of 16 relationships between trade openness and real economic growth in Turkey, we took into account the effects of third variables that stem from a production function framework. This framework also helps prevent the detection of possibly spurious Granger-causality results that might emerge from not including third variables that affect both economic growth and trade openness variables. Using the PWT 9.0 database, we used the capital-stock-to-GDP ratio, the number of people engaged in working, government-consumption-to-GDP ratio, and the ratio of exports prices to imports prices (terms of trade) variables as control variables. We have also considered including the foreign direct investment (FDI) variable in the control variable set. Nevertheless, the FDI variable was not included in the PWT 9.0. The FDI series on Turkey from the World Bank starts in 1974, leading to a much shorter sample period. Given the resulting much shorter sample and the results from the earlier literature that FDI does not significantly affect economic growth in Turkey (as discussed above), we did not include the FDI variable among the control variables. Our study differs from earlier studies as we test for the presence of Granger-causal links between economic growth and trade openness in the frequency domain not only for the overall sample (1950–2014), but also for different sample periods based on a rolling estimation window. This approach allows us to determine how the Granger-causal relationships evolve over time while at the same time yielding further information on the shortand long-term nature of (if any) Granger-causal relationships at given estimation windows. We start our analysis by first testing for the stationarity of the variables by means of the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests. We included a constant term and trend in the specification of the unit root tests and select the optimal lag lengths according to the Akaike information criterion (AIC). The unit root test results are presented in Table 1. Table 1. Unit Root Tests. Level First Differences Variables ADF PP ADF PP GDP −4.157 *** −4.158 *** −9.234 *** −16.326 *** [0.008] [0.008] [0.000] [0.000] Trade Openness −1.633 −1.907 −2.771 −7.093 *** [0.766] [0.639] [0.213] [0.000] Capital Stock −2.816 * −2.685 * −5.496 *** −9.417 *** [0.061] [0.082] [0.000] [0.000] Labor −5.678 *** −5.304 *** −7.214 *** −7.217 *** [0.000] [0.000] [0.000] [0.000] Government Expenditure −2.097 −2.752 −10.162 *** −16.090 *** [0.537] [0.220] [0.000] [0.000] Terms of trade −5.500 *** −5.404 *** −11.249 *** −30.255 *** [0.000] [0.0000] [0.000] [0.000] Note: The figures in square brackets show the probability (p-values) of rejecting the null hypothesis of non-stationarity. ***, **, and * indicate that the series in question is stationary at the 1%, 5%, and 10% significance level, respectively. ADF stands for the Augmented Dickey Fuller test and PP stands for the Phillips-Perron test. The test results in Table 1show that real GDP, labor, and terms of trade are stationary at 1% significance level. Although capital was found be stationary at 10% significance level, we can reject the null hypothesis for the trade openness and expenditure at first differences. It should be noted that Breitung and Candelon (2006) indicated that the frequency domain test is robust for the lag-augmented Granger-causality test proposed by Toda and Yamamoto (1995), and hence, we considered one for the maximum order of integration of variables in the test procedure. After confirming the integration levels of the variables, we employ a bivariate VAR model with control variables and with lag-lengths determined according to the AIC. Then, we employed (in line Economies 2019,7, 41 15 of 16 Gräbner, Claudius, Philipp Heimberger, Jakob Kapeller, and Florian Springholz. 2018. Measuring Economic Openness: A Review of Existing Measures and Empirical Practices. 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