Abstract
This paper utilizes panel VAR analysis to investigate the relationship between the sentiment of Japanese citizens toward their trading partners and the flow of trade. Using a system GMM approach, short- and long-run restrictions are applied in a panel VAR setting for the first time to identify structural shocks to sentiment, imports, exports, and the exchange rate. Results show that people's collective psychological workings and trade patterns are interrelated. Evidence of a strong asymmetric relationship between imports and sentiment is found: increases in sentiment lead to higher imports, but a positive shock to imports causes significant drops in sentiment.
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Notes
The data can be found at the website: http://www8.cao.go.jp/survey/index-gai.html. The data are available from the authors upon request.
Data were also deflated by the US CPI, following other work in the literature. All results are similar and are available from the authors upon request.
Panel VAR estimation was performed in RATS 8.0.
For more information on specific requirements for identifying restrictions in structural VAR models, see Rubio-Ramirez et al. [2010].
The Maddala and Wu [1999] test is a non-parametric panel unit root test. The Pesaran and Maddalu and Wu tests were performed in MATLAB using the code provided on Christopher Hurlin's web page, http://www.univ-orleans.fr/deg/masters/ESA/CH/churlin_R.htm. The other tests were run in Stata.
The Pedroni tests were conducted in RATS and the Westerlund tests were conducted in Stata.
All estimates discussed in this section are similar if Russia is included in the panel VAR, except that estimated standard errors are significantly larger.
Japan has no formal Economic Partnership Agreements (EPA) with the countries in the sample and, as such, no policy variables are added to the VAR to capture this potential effect. Japan has been one of the slowest among developed countries to develop such EPAs largely due to its agricultural policies.
The sum of the variance decompositions does not necessarily sum to 100 as the median figure from the 1,000 bootstrap samples at each horizon is reported in the table.
References
Amuedo-Durantes, C., S. Pozo, and C. Vargas-Silva . 2010. Remittances in Small Island Developing States. Journal of Development Studies, 46 (5): 941–960.
Arellano, M., and O. Bover . 1995. Another Look at the Instrumental Variable Estimation of Error-components Models. Journal of Econometrics, 68 (1): 29–51.
Arize, A.C. 2002. Imports and Exports in 50 Countries Test of Cointegration and Structural Breaks. International Review of Economics and Finance, 11 (1): 101–111.
Baker, M., and J. Wurgler . 2004. Investor Sentiment and the Cross-section of Stock Returns, National Bureau of Economic Research Working Papers: 10449.
Bird, R., and L. Casavecchia . 2007. Sentiment and Financial Health Indicators for Value and Growth Stocks: The European Experience. European Journal of Finance, 13 (8): 769–793.
Bordo, M.D., J.L. Lane, and A. Redish . 2010. Deflation, Productivity Shocks and Gold: Evidence from the 1880–1914 Period. Open Economies Review, 21 (4): 515–546.
Breitung, J. 2000. The Local Power of Some Unit Root Tests for Panel Data, in Advances in Econometrics, 15: Non-Stationary Panels, Panel Cointegration, and Dynamic Panels, edited by B. Baltagi, T.H. Fomby, and R.C. Hill. United Kingdom: JAI Press.
Celik, S., and Y. Ozerkek . 2009. Panel Cointegration Analysis of Consumer Confidence and Personal Consumption in the European Union. Journal of Business Economics and Management, 10 (2): 161–168.
Choe, J.I. 2003. Do Foreign Direct Investment and Gross Domestic Investment Promote Economic Growth? Review of Development Economics, 7 (1): 44–57.
Easaw, J.Z., D. Garrett, and S.M. Heravi . 2005. Does Consumer Sentiment Accurately Forecast UK Household Consumption? Are There Any Comparisons to be Made with the US? Journal of Macroeconomics, 27 (3): 517–532.
Erdil, E., and I.H. Yetkiner . 2009. The Granger-causality between Health Care Expenditure and Output: A Panel Data Approach. Applied Economics, 41 (4): 511–518.
Foroutan, F., and L. Pritchett . 1993. Intra-Sub-Saharan African Trade: Is It Too Little? Journal of African Economics, 2 (1): 74–105.
Frankel, J.A., and S.J. Wei . 1995. European Integration and the Regionalization of World Trade and Currencies: The Economics and the Politics, in Monetary and Fiscal Policy in an Integrated Europe, edited by B. Eichengreen, F. Frieden, and J. von Hagen. New York: Springer-Verlag.
Frankel, J.A., E. Stein, and S.J. Wei . 1997. Regional Trading Blocs in the World Economic System. Washington, DC: Institute for International Economics.
Gali, J. 1992. How Well Does the IS-LM Model Fit Post-war Data? The Quarterly Journal of Economics, 107 (2): 709–738.
Garrett, T.A., R. Hernandez-Murillo, and M.T. Owyang . 2005. Does Consumer Sentiment Predict Regional Consumption? Federal Reserve Bank of St. Louis Review, 87 (2): 123–135.
Gilchrist, S., and C.P. Himmelberg . 1995. Evidence on the role of cash flow for investment. Journal of Monetary Economics, 36 (3): 541–572.
Gilchrist, S., and C.P. Himmelberg . 1997. Investment, Fundamentals and Finance, in NBER Macroeconomics Annual, edited by B.S. Bernanke, and J.J. Rotemberg. Cambridge: MIT Press.
Gomes, O. 2009. On the Stability of Endogenous Growth Models: An Evaluation of the Agent's Response to Output Fluctuations. Journal of Economic Studies, 36 (1): 17–35.
Gomes, O. . 2010a. Consumer Confidence, Endogenous Growth and Endogenous Cycles. Journal of Economic Studies, 37 (4): 377–404.
Gomes, O. . 2010b. Deterministic Randomness in a Model of Finance and Growth. Journal of Evolutionary Economics, 20 (1): 95–114.
Granger, C.W.J. 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37 (3): 424–438.
Guo, R. 2004. How Culture Influences Foreign Trade: Evidence from the U.S. and China. The Journal of Socio-Economics, 33 (6): 785–812.
Guo, R. . 2007. Linguistic and Religious Influences on Foreign Trade: Evidence from East Asia. Asian Economic Journal, 21 (1): 101–121.
Hadri, K. 2000. Testing for Stationarity in Heterogeneous Panel Data. Econometrics Journal, 3 (2): 148–161.
Hansen, H., and J. Rand . 2004. On the causal links between FDI and growth in developing countries, mimeo, Development Economics Research Group (DERG), Institute of Economics, University of Copenhagen.
Havrylyshyn, O., and L. Pritchett . 1991. European Trade Patterns after the Transition, Policy, Research and External Affairs Working Paper No. 74. Washington, DC: World Bank.
Holtz-Eakin, D., W. Newey, and H. Rosen . 1985. Implementing Causality Tests with Panel Data with an Example from Local Public Finance, NBER Technical Working Paper 48.
Holtz-Eakin, D., W. Newey, and H. Rosen . 1988. Estimating Vector Autoregressions with Panel Data. Econometrica, 56 (6): 1371–1395.
Howrey, E.P. 2001. The Predictive Power of the Index of Consumer Sentiment. Brookings Papers on Economic Activity, 32 (1): 175–207.
Hsiao, C. 1986. Analysis of Panel Data. Cambridge: Cambridge University Press.
Hsiao, C. . 1989. Modeling Ontario Regional Electricity System Demand Using a Mixed Fixed and Random Coefficients Approach. Regional Science and Urban Economics, 19 (4): 565–587.
Hurlin, C. 2007. Testing for Granger Non Causality in Heterogenous Panels, Document de Recherche LEO, 2007–2010.
Hurlin, C., and B. Venet . 2001. Granger Causality Tests in Panel Data Models with Fixed Coefficients, Working Paper Eurisco 2001–2009. University of Paris Dauphine.
Husted, S. 1992. The Emerging U.S. Current Account Deficit in the 1980s: A Cointegration Analysis. The Review of Economics and Statistics, 74 (1): 159–166.
Hutchinson, W.K. 2002. Does Ease of Communication Increase Trade? Commonality of Language and Bilateral Trade. Scottish Journal of Political Economy, 49 (5): 544–556.
Im, K.S., M.J. Pesaran, and Y. Shin . 2003. Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 115 (1): 53–74.
Jones, C.I. 1995. Time Series Tests of Endogenous Growth Models. The Quarterly Journal of Economics, 110 (2): 495–525.
Kandil, M., and I.A. Mirzaie . 2011. Consumption, Credit, and Macroeconomic Policies: Theory and Evidence from the United States. Global Economic Review, 40 (3): 323–360.
Kim, S., and Y. Goo . 2008. Is Consumer Sentiment Index Useful in Predicting Household Consumption? A Directional Analysis with Korean Data. Journal of the Korean Economy, 9 (2): 205–235.
Lamdin, D.J. 2008. Does Consumer Sentiment Foretell Revolving Credit Use? Journal of Family and Economic Issues, 29 (2): 279–288.
Levin, A., and C.F. Lin . 1992. Unit Root Tests in Panel Data: Asymptotic and Finite-sample Properties, Discussion Paper 92–23, San Diego: University of California.
Levin, A., C.F. Lin, and C. Chu . 2002. Unit Root Tests in Panel Data: Asymptotic and Finite-sample Properties. Journal of Econometrics, 108 (1): 1–24.
Lewer, J.J., and H.V. Berg . 2007. Religion and International Trade: Does the Sharing of a Religious Culture Facilitate the Formation of Trade Networks? American Journal of Economics & Sociology, 66 (4): 765–794.
Love, I., and L. Zicchino . 2006. Financial Development and Dynamic Investment Behavior: Evidence from Panel VAR. The Quarterly Review of Economics and Finance, 46 (2): 190–210.
Maddala, G., and S. Wu . 1999. A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test. Oxford Bulletin of Economics and Statistics, 61: 631–652.
Pedroni, P. 1999. Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics, 61: 653–670.
Pesaran, M. 2007. A Simple Panel Unit Root Test for Cross-section Dependence. Journal of Applied Econometrics, 22 (2): 265–312.
Rauch, J.E. 1999. Networks Versus Markets in International Trade. Journal of International Economics, 48 (1): 7–35.
Roodman, D. 2009. A Note on the Theme of Too Many Instruments. Oxford Bulletin of Economics and Statistics, 71 (1): 135–158.
Rubio-Ramirez, J.F., D.F. Waggoner, and T. Zha . 2010. Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference. Review of Economic Studies, 77 (2): 665–696.
Schmeling, M. 2009. Investor Sentiment and Stock Returns: Some International Evidence. Journal of Empirical Finance, 16 (3): 394–408.
Sims, C.A., and T. Zha . 1999. Error Bands for Impulse Responses. Econometrica, 67 (5): 1113–1156.
Souleles, N.S. 2004. Expectations, Heterogeneous Forecast Errors, and Consumption: Micro Evidence from the Michigan Consumer Sentiment Surveys. Journal of Money, Credit and Banking, 36 (1): 39–72.
Weinhold, D. 1999. A Dynamic ‘Fixed Effects’ Model for Heterogeneous Panel Data, Unpublished Manuscript, London School of Economics.
Westerhoff, F.H. 2008. Consumer Sentiment and Business Cycles: A Neimark-Sacker Bifurcation Scenario. Applied Economic Letters, 15 (15): 1201–1205.
Westerlund, J. 2007. Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and Statistics, 69 (6): 709–748.
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APPENDIX
APPENDIX
Hurlin panel granger causality test statistics
In the case of exchange rates granger causing imports, the Hurlin test is based on the following panel regression with stationary variables:
where θ i =(θ ii (1), …, θ ii (K)). Individual effects α i are assumed to be fixed, and the lag orders, K, are also assumed to be identical across the panel (two lags are used in the paper). Additionally, the autoregressive coefficients, γ i (k), and the regression coefficients, θ i (k), vary across panels. The null hypothesis is: H0: θ i =0, ∀i=1, …, N, and the alternative hypothesis is: H1: θ i =0, ∀i=1, …, N1 and θ i ≠0, ∀i=N1+1, N2+1, …, N, where N1 is unknown but satisfies 0⩽N1/N<1. N=4 in Table 2.
The test statistics is simply the average of individual Wald statistics from the individual country granger causality test for i=1, …, N. Let W N,T Hnc be the average of the test statistics. Then,
where W i,T denotes the individual Wald statistics from the individual country granger causality test. Hurlin [2007] showed that this statistic converges to a chi-squared distribution with K degrees of freedom. Moreover, W N,T Hnc converges toward a normal distribution when T goes to infinity and N goes to infinity. Specifically, let Z N,T Hnc be the corresponding standardized statistic:
Then, 
. Furthermore, Hurlin [2007] also proposed that for a small sample, T, the following approximated standard statistic, denoted by Ẑ
N,T
Hnc, follows approximately the same distribution as the standardized average Wald statistics Z
N,T
Hnc. Ẑ
N,T
Hnc 
, where Ẑ
N,T
Hnc takes the following form:
Table 2 examines panel granger causality for all possible combinations of the four panel variables sentiment, imports, exports, and exchange rates using statistics created for (A2)–(A4).