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Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Models

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  • Hayakawa, K.
  • Pesaran, M.H.

Abstract

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran, and Tahmiscioglu (2002) to the case where the errors are crosssectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model. It is shown that the transformed maximum likelihood estimator continues to be consistent even in the presence of cross-sectional heteroskedasticity. We also obtain standard errors that are robust to cross-sectional heteroskedasticity of unknown form. By means of Monte Carlo simulation, we investigate the finite sample behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood estimator outperforms the GMM estimators in almost all cases.

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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 1224.

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Date of creation: 09 May 2012
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Handle: RePEc:cam:camdae:1224

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Web page: http://www.econ.cam.ac.uk/index.htm

Related research

Keywords: Dynamic Panels; Cross-sectional heteroskedasticity; Monte Carlo simulation; GMM estimation;

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  1. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
  2. Whitney K. Newey & Frank Windmeijer, 2009. "Generalized Method of Moments With Many Weak Moment Conditions," Econometrica, Econometric Society, vol. 77(3), pages 687-719, 05.
  3. Maurice J.G. Bun & Jan F. Kiviet, 2002. "The Effects of Dynamic Feedbacks on LS and MM Estimator Accuracy in Panel Data Models," Tinbergen Institute Discussion Papers 02-101/4, Tinbergen Institute, revised 19 Feb 2004.
  4. 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.
  5. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, edition 1, number 9780198774488, September.
  6. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
  7. Michael Binder & Cheng Hsiao & M. Hashem Pesaran, 2000. "Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration," Banco de Espa�a Working Papers 0005, Banco de Espa�a.
  8. Stephen Bond & Frank Windmeijer, 2005. "Reliable Inference For Gmm Estimators? Finite Sample Properties Of Alternative Test Procedures In Linear Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 24(1), pages 1-37.
  9. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
  10. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
  11. Kruiniger, Hugo, 2008. "Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model," Journal of Econometrics, Elsevier, vol. 144(2), pages 447-464, June.
  12. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 1-9, January.
  13. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
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