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Monetary Policy and the Time-Varying Spatial Effects of Bilateral Trade: Evidence from China-ASEAN-5 Countries

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Abstract

This paper examines the China–ASEAN (Association of Southeast Asian Nations)-5 bilateral trade balances by using a time-space simultaneous gravity model with a panel smooth transition regression specification. This model can investigate both internal and external bilateral trade. The empirical results show that a larger real interest rate differential between China and a member country of the ASEAN-5 would switch the negative spatial effect to a positive one, causing a higher average spatial effect from the bilateral trade balance. The time-varying marginal effects of the determinants of bilateral trades are verified. However, the geographical distance of each country pair remains the most important deterrent to China–ASEAN-5 bilateral trade.

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Notes

  1. The first law of Geography by Tobler (1970) states that everything is related to everything else, but closer things are more closely related than distant things. The regional scientists then began to give relevance to this aspect. In the International Trade, spatial dependence can be justified by the role of the third country effect. Anselin (1988) indicates that there are two types of spatial effects: spatial dependence and heterogeneity. The former is caused by various degrees of spatial aggregation, spatial externalities and spillover effects, and spatial structure, and the latter results from heterogeneity inherent in the delineation of spatial units and from contextual variation over space.

  2. There are some more methods to account for the multilateral resistance terms, for example, the Bonus vetus OLS (Baier and Bergstrand 2009) and the Moving Average component (Behrens et al. 2007).

  3. The methodologies for flow data have been implemented by Lesage and Pace (2008) and Fischer and Griffith (2008).

  4. Anderson and van Wincoop (2003) indicate that the export costs for exports from country i to country j is the cost of exporting from country i to country j relative to the cost of exporting from country i to all other potential importing competitors of j. That is, there exist third country effects. However, in typical gravity models, these third country effects are assumed to be captured by the country pair fixed effects. There are at least two problems that stem from employing this typical approach. First, the country pair fixed effects undoubtedly reflect factors in addition to the third country effects. Second, the country pair fixed effects are constant over time; however, the third country effects need not be. The considerations of spatial effects and the PSTR specification of the gravity model can simultaneously resolve these two problems.

  5. Florax et al. (2003) indicate that the classical forward stepwise method is a proper instrument for choosing the optimal spatial effect model. Following Florax et al. (2003), we also perform the LM (Lagrange multiplier) tests of two types of spatial models, a spatial lag model and a spatial error model, and the testing results show that the former is superior to the latter. Thus, we choose the model as shown in Eq. (1).

  6. Due to the presence of fixed individual and time effects, the regressors do not include time invariant or individually invariant regressors.

  7. González et al. (2005) argue that from an empirical point of view, it is sufficient to consider only the cases of two regimes to capture the nonlinearities due to regime switching.

  8. For the testing procedures, see Teräsvirta (1994).

  9. In spatial econometrics literature, the researchers frequently consider some benchmark variables such as contiguity dummy and language dummy; however, in the PSTR framework, the fixed effects can capture the characteristics of these benchmark variables. Other benchmark variables such as political freedom and economic freedom are ignored due to unavailable data.

  10. We have also tried replacing d 2 ij with d ij to estimate Eq. (2); however, the estimation result shows a larger value for the AIC (Akaike information criterion). That is, a constant spatial effect does not hold.

  11. We also try using the US fund rates as the transition variable because the monetary policy adopted by the Federal Reserve has an announcement effect that influences the remaining countries in the world to adopt a corresponding policy (Wu et al. 2013). However, the estimation result cannot support a PSTR specification for the gravity model of trade. The change of real exchange rate is another candidate of transition variable. However, we are disappointed at the estimation results, because the optimal lag length of the change of real exchange rate is five. In this case, the monetary policy will influence a country’s trade balances after five years, which violates general economic intuition. Thus, we do not add the estimation results of these two transition variables into our paper.

  12. For the grid approach, see Colletaz and Hurlin (2006).

  13. For more details, see Fouquau et al. (2008).

  14. The descriptive statistics and panel unit root tests of the variables used in this paper are available upon request.

  15. The testing results are not listed here; however, they are available upon request.

  16. In estimating Eq. (1), we ignore two regressors due to their statistical insignificance: time fixed effects and the volatility of the real exchange rate. In addition, we use the SUR model to resolve the problem of contemporaneous cross-equation error correlation.

  17. Spielman (2012) indicates that policy intervention may provide another interpretation about negative spatial effect. In urban disciplines negative spatial autocorrelation can results from a deliberative intervention into established patterns of development, such as constructing a new high density sub-division amidst low density suburban sprawl. Such interventions often have important policy implications and negative spatial autocorrelation can provide quasi-experimental conditions for the study of the built environment.

  18. According to the testing results in Table 2, the cases of m = 2, r = 1, and d = 0, 1, 3 can pass the nonlinearity testing; however, their corresponding estimated threshold values are outside of the sample range of the real interest rate differential. Thus, we do not display their estimated results in Table 3.

  19. The digits can be obtained from our original sample data and the estimated parameters of Eq. (2).

  20. An insignificant estimated parameter in the (P)STAR model does not mean that the nonlinear model fails (Terisverta 1994). Some previous studies had gotten these results (see for example, Béreau et al. 2010; Jude 2010). Besides, the linearity and no remaining nonlinearity tests all support the PSTR model is optimal, and the estimated coefficients in at least one of the two regimes are statistically significant.

References

  • Anderson, J. E., & van Wincoop, E. (2003). Gravity with Gravitas: a solution to the border puzzle. American Economic Review, 93(1), 171–192.

    Google Scholar 

  • Anselin, L. (1988). Spatial econometrics: Methods and models. the Netherlands: Kluwer.

    Book  Google Scholar 

  • Anselin, L. (2009). Spatial regression. In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis (pp. 255–276). Los Angeles: Sage.

    Google Scholar 

  • Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial panel econometrics. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (pp. 625–660). Berlin: Springer.

    Chapter  Google Scholar 

  • Baier, S. L., & Bergstrand, J. H. (2009). Bonus vetus OLS: a simple method for approximating international trade-cost effects using the gravity equation. Journal of International Economics, 77(1), 77–85.

    Article  Google Scholar 

  • Balasubramaniam, A., Puah, C.-H., & Mansor, S. A. (2012). Economic interdependence: evidence from China and ASEAN-5 countries. Modern Economy, 3, 122–125.

    Article  Google Scholar 

  • Behrens, K., Ertur, C., & Koch, W. (2007). Dual gravity: Using spatial econometrics to control for multilateral resistance. Louvain-la-Neuve: CORE.

    Google Scholar 

  • Bénassy-Quéré, A., &Lahrèche-Révil, A. (2003). Trade linkages and exchange rates in Asia: the role of China. CEPII Working paper No 2003–21.

  • Béreau, S., Villavicencio, A. L., & Mignon, V. (2010). Nonlinear adjustment of the real exchange rate towards its equilibrium value: a panel smooth transition error correction modeling. Economic Modelling, 27, 404–416.

    Article  Google Scholar 

  • Colletaz, G., & Hurlin, C. (2006). Threshold effects in the public capital productivity: an international panel smooth transition approach. Working Paper, University of Orléans.

  • Devadason, E. S. (2011). Reorganization of intra-ASEAN 5 trade flows: the ‘China factor’. Asian Economic Journal, 25(2), 129–149.

    Article  Google Scholar 

  • Elhorst, J. P. (2003). Specification and estimation of spatial panel data models. International Regional Science Review, 26(3), 244–268.

    Article  Google Scholar 

  • Fischer, M. M., & Griffith, D. A. (2008). Modeling spatial autocorrelation in spatial interaction data: an application to patent citation data in the European Union. Journal of Regional Science, 48(5), 969–989.

    Article  Google Scholar 

  • Florax, R. J. G. M., Folmer, H., & Rey, S. J. (2003). Specification searches in spatial econometrics: the relevance of Hendry’s methodology. Regional Science and Urban Economics, 33, 557–579.

    Article  Google Scholar 

  • Fok, D., van Dijk, D., & Franses, P. (2004). A multi-level panel STAR model for US manufacturing sectors. Working Paper, University of Rotterdam.

  • Fouquau, J., Hurlin, C., & Rabaud, I. (2008). The Feldstein–Horioka puzzle: a panel smooth transition regression approach. Economic Modelling, 25, 284–299.

    Article  Google Scholar 

  • González, A., Teräsvirta, T., & van Dijk, D. (2005). Panel smooth transition regression models. Research paper, 165, Sidney Quantitative Finance Research Centre, University of Technology.

  • Greenaway, D., & Milner, C. (1986). The economics of intra-industry trade. Oxford: Basil Blackwell.

    Google Scholar 

  • Gruen, D. W. R., & Wilkinson, J. (1994). Australia’s real exchange rate. Is it explained by the terms of trade or by real interest differentials? The Economic Record, 70, 204–219.

    Article  Google Scholar 

  • Imbs, J., Mumtaz, H., Ravn, M. O., & Rey, H. (2003). Nonlinearities and real exchange rate dynamics. Journal of the European Economic Association, 1(2–3), 639–649.

    Article  Google Scholar 

  • Ivrandi, M., & Guloglu, B. (2010). Monetary shocks, exchange rates and trade balances: evidence from inflation targeting countries. Economic Modelling, 27(5), 1144–1155.

    Article  Google Scholar 

  • Jude, E. C. (2010). Financial development and growth: a panel smooth regression approach. Journal of Economic Development, 35(1), 15–33.

    Google Scholar 

  • Kepaptsoglou, K., Tsamboulas, D. A., Karlaftis, M. G., & Marzano, V. (2009). Free trade agreements effects in the Mediterranean Region: an analytic approach based on sure gravity model. Transportation Research Record, 2097, 88–96.

    Article  Google Scholar 

  • Khan, M. Z. S., & Hossaian, M. I. (2010). A model of bilateral trade balance: extensions and empirical tests. Economic Analysis and Policy, 40(3), 377–391.

    Article  Google Scholar 

  • Krugman, P. R. (1991). Increasing returns and economic geography. Journal of Political Economy, 99, 483–499.

    Article  Google Scholar 

  • Lau, E., & Lee, K. P. (2008). Interdependence of income between China and ASEAN-5 countries. Journal of Chinese Economic and Foreign Trade Studies, 1(2), 148–161.

    Article  Google Scholar 

  • Lee, J., & Chinn, M. (2006). Current account and the real exchange rate in the G7 countries. Journal of International Money and Finance, 25, 257–274.

    Article  Google Scholar 

  • LeSage, J. P., & Pace, R. K. (2008). Spatial econometric modeling of origin-destination flows. Journal of Regional Science, 48(5), 941–967.

    Article  Google Scholar 

  • Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–23.

    Article  Google Scholar 

  • Mukherjee, D., & Pozo, S. (2011). Exchange-rate volatility and trade: a semiparametric approach. Applied Economics, 43(13), 1617–1627.

    Article  Google Scholar 

  • Ong, H. B., Puah, C. H., & Habibullah, M. S. (2006). Inter-dependence of ASEAN business cycles. Frontiers in Finance and Economics, 3(1), 69–78.

    Google Scholar 

  • Porojian, A. (2001). Trade flows and spatial effects: the gravity model revisited. Open Economies Review, 12(3), 265–280.

    Article  Google Scholar 

  • Pradhan, R. P. (2010). Interdependence of FDI between India and ASEAN-5: evidence from causality approach. International Business Research, 3(4), 156–167.

    Article  Google Scholar 

  • Roberts, B. A. (2004). A gravity study of the proposed China-ASEAN free trade area. International Trade Journal, 18(4), 335–353.

    Article  Google Scholar 

  • Rose, A. K. (2000). Currency unions-one money, one market: the effect of Common Currencies on trade. Economic Policy, 15(30), 7–45.

    Article  Google Scholar 

  • Spielman, S. E. (2012). Exceptions to the law: negative spatial autocorrelation in egocentric spatial analysis. GIScience 2012, Columbus Ohio.

  • Tan, S.-H., Habibullah, M.-S., & Mohamed, A. (2010). Asymmetric effects of monetary policy in ASEAN-4 economies. International Research Journal of Finance and Economics, 44, 31–44.

    Google Scholar 

  • Tang, D. (2005). Effects of the regional trading arrangements on trade: evidence from the NAFTA, ANZCER and ASEAN Countries, 1989–2000. Journal of International Trade & Economic Development, 14(2), 241–265.

    Article  Google Scholar 

  • Teräsvirta, T. (1994). Specification, estimation and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89, 208–218.

    Google Scholar 

  • Thorbecke, W. (2011). The Effect of exchange rate changes on trade in East Asia. Journal of Commerce, Economics and Policy, 2(1), 85–102.

    Article  Google Scholar 

  • Tinbergen, J. (1962). Shaping the world economy: Suggestions for an international economic policy. New York: The Twentieth Century Fund.

    Google Scholar 

  • Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.

  • Wu, P.-C., Liu, S.-Y., & Pan, S.-C. (2013). Nonlinear bilateral trade balance-fundamentals nexus: a panel smooth transition regression approach. International Review of Economics and Finance, 27, 318–329.

  • Yew, S.-Y., Yong, C.-C., & Tan, H.-B. (2010). Impact of economic integration on foreign direct investment into ASEAN5. Malaysian Journal of Economic Studies, 47(1), 73–90.

  • Zhang, Z., & Ow, C. H. (1996). Trade interdependence and direct foreign investment between ASEAN and China. World Development, 24, 155–170.

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  1. Department of International Business, Chung Yuan Christian University, No. 200, Chung Pei Rd., Chung Li, Taiwan, 320, Republic of China

    Po-Chin Wu & Shiao-Yen Liu

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  1. Po-Chin Wu
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Correspondence to Po-Chin Wu.

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Table 6 Data measurement
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Wu, PC., Liu, SY. Monetary Policy and the Time-Varying Spatial Effects of Bilateral Trade: Evidence from China-ASEAN-5 Countries. Appl. Spatial Analysis 10, 103–120 (2017). https://doi.org/10.1007/s12061-015-9175-x

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