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Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence

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  • Arturas Juodis

Abstract

This paper considers the Panel Vector Autoregressive Models of order 1 (PVAR(1)) with possibly spatially dependent error terms. We propose a simple Method of Moments based cointegration test using the rank test of Kleibergen and Paap (2006) for fixed number of time observations. The test is shown to be robust to spatial dependence, cross-sectional and time series heteroscedasticity as well as unbalanced panels. The main novelty of our approach is that we fully exploit the "weakness" of the Anderson and Hsiao (1982) moment conditions in construction of the new test. The finite-sample performance of the proposed test statistic is investigated using the simulated data. The results show that for most scenarios the method performs well in terms of both size and power. The proposed test is applied to employment and wage equations using Spanish firm data of Alonso-Borrego and Arellano (1999) and the results show little evidence for cointegration.

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Bibliographic Info

Paper provided by Universiteit van Amsterdam, Dept. of Econometrics in its series UvA-Econometrics Working Papers with number 13-08.

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Date of creation: 03 Oct 2013
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Handle: RePEc:ame:wpaper:1308

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Postal: Dept. of Econometrics, Universiteit van Amsterdam, Valckenierstraat 65, NL - 1018 XE Amsterdam, The Netherlands
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Web page: http://www.ase.uva.nl/uva-econometrics
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  1. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, 07.
  2. David Roodman, 2007. "A Note on the Theme of Too Many Instruments," Working Papers 125, Center for Global Development.
  3. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, 07.
  4. Robertson, Donald & Sarafidis, Vasilis & Symons, James, 2010. "IV Estimation of Panels with Factor Residuals," MPRA Paper 26166, University Library of Munich, Germany.
  5. Hayakawa, Kazuhiko, 2009. "On the effect of mean-nonstationarity in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 153(2), pages 133-135, December.
  6. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2006. "Panel Unit Root Tests and Spatial Dependence," Center for Policy Research Working Papers 88, Center for Policy Research, Maxwell School, Syracuse University.
  7. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
  8. "Hayakawa, Kazuhiko", 2008. "Dynamic Panel Data Models―A Survey―," Economic Review, Hitotsubashi University, vol. 59(2), pages 112-125, April.
  9. M. Hashem Pesaran & Elisa Tosetti, 2011. "Large panels with common factors and spatial correlation," Post-Print peer-00796743, HAL.
  10. Sarafidis, Vasilis & Wansbeek, Tom, 2010. "Cross-sectional Dependence in Panel Data Analysis," MPRA Paper 20367, University Library of Munich, Germany.
  11. Richard Paap & Frank Kleibergen, 2004. "Generalized Reduced Rank Tests using the Singular Value Decomposition," Econometric Society 2004 Australasian Meetings 195, Econometric Society.
  12. Manuel Arellano, 2003. "Modelling Optimal Instrumental Variables For Dynamic Panel Data Models," Working Papers wp2003_0310, CEMFI.
  13. Jan Mutl, 2002. "Panel VAR Models with Spatial Dependence," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A5-2, International Conferences on Panel Data.
  14. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
  15. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
  16. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
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