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Application of Bootstrap Methods in Investigation of Size of the Granger Causality Test for Integrated VAR Systems

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  • Lukasz Lach

    (University of Science and Technology, Poland)

Abstract

This paper examines the size performance of the Toda-Yamamoto test for Granger causality in the case of trivariate integrated and cointegrated VAR systems. The standard asymptotic distribution theory and the residual-based bootstrap approach are applied. A variety of types of distribution of error term is considered. The impact of misspecification of initial parameters as well as the influence of an increase in sample size and number of bootstrap replications on size performance of Toda-Yamamoto test statistics is also examined. The results of the conducted simulation study confirm that standard asymptotic distribution theory may often cause significant over-rejection. Application of bootstrap methods usually leads to improvement of size performance of the Toda-Yamamoto test. However, in some cases the considered bootstrap method also leads to serious size distortion and performs worse than the traditional approach based on ÷2 distribution.

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Bibliographic Info

Article provided by University of Primorska, Faculty of Management Koper in its journal Managing Global Transitions.

Volume (Year): 8 (2010)
Issue (Month): 2 ()
Pages: 167-186

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Handle: RePEc:mgt:youmgt:v:8:y:2010:i:2:p:167-186

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Related research

Keywords: bootstrap methods; simulation; Granger causality; bootstrap methods; simulation; Granger causality; VAR models models;

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References

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  1. Mantalos Panagiotis, 2000. "A Graphical Investigation of the Size and Power of the Granger-Causality Tests in Integrated-Cointegrated VAR Systems," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(1), pages 1-18, April.
  2. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
  3. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  4. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-46, March.
  5. Hyllerberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal Integration And Cointegration," Papers 0-88-2, Pennsylvania State - Department of Economics.
  6. R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1489-1500.
  7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  8. A. Hatemi-J, 2003. "A new method to choose optimal lag order in stable and unstable VAR models," Applied Economics Letters, Taylor & Francis Journals, vol. 10(3), pages 135-137.
  9. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
  10. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
  11. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
  12. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  13. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
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Citations

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Cited by:
  1. Gurgul, Henryk & Lach, Łukasz, 2012. "The electricity consumption versus economic growth of the Polish economy," MPRA Paper 52233, University Library of Munich, Germany.
  2. Gurgul, Henryk & Lach, lukasz, 2011. "The role of coal consumption in the economic growth of the Polish economy in transition," Energy Policy, Elsevier, vol. 39(4), pages 2088-2099, April.
  3. Lukasz Lach & Henryk Gurgul, 2010. "International trade and economic growth in the Polish economy," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 3, pages 5-29.
  4. A. Talha Yalta & Hatice Cakar, 2012. "Energy Consumption and Economic Growth in China: A Reconciliation," Working Papers 1202, TOBB University of Economics and Technology, Department of Economics.
  5. Gurgul, Henryk & Lach, Łukasz, 2011. "The interdependence between energy consumption and economic growth in the Polish economy in the last decade," MPRA Paper 52283, University Library of Munich, Germany.
  6. Lukasz Lach, 2011. "Impact of hard coal usage for metal production on economic growth of Poland," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 9, pages 103-120.
  7. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
  8. Lach, Łukasz, 2010. "Fixed capital and long run economic growth: evidence from Poland," MPRA Paper 52280, University Library of Munich, Germany.
  9. Di Iorio, Francesca & Triacca, Umberto, 2011. "Testing for non-causality by using the Autoregressive Metric," MPRA Paper 29637, University Library of Munich, Germany.

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