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Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

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  • Maurice J.G. Bun

    ()
    (University of Amsterdam)

Abstract

Approximation formulae are developed for the bias of ordinary andgeneralized Least Squares Dummy Variable (LSDV) estimators in dynamicpanel data models. Results from Kiviet (1995, 1999) are extended tohigher-order dynamic panel data models with general covariancestructure. The focus is on estimation of both short- and long-runcoefficients. The results show that proper modelling of thedisturbance covariance structure is indispensable. The biasapproximations are used to construct bias corrected estimators whichare then applied to quarterly data from 14 European Union countries.Money demand functions for M1, M2 and M3 are estimated for the EUarea as a whole for the period 1991:I-1995:IV. Significant spilloversbetween countries are found reflecting the dependence of domesticmoney demand on foreign developments. The empirical results show thatin general plausible long-run effects are obtained by the biascorrected estimators. Moreover, bias correction can be substantialunderlining the importance of more refined estimation techniques.Also the efficiency gains by exploiting the heteroscedasticity andcross-correlation patterns between countries are sometimesconsiderable.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 01-007/4.

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Date of creation: 17 Jan 2001
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Handle: RePEc:dgr:uvatin:20010007

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  1. Carlo C. A. Winder & Martin M. G. Fase, 1998. "Wealth and the demand for money in the European union," Empirical Economics, Springer, vol. 23(3), pages 507-524.
  2. Kiviet, Jan F. & Phillips, Garry D. A. & Schipp, Bernhard, 1995. "The bias of OLS, GLS, and ZEF estimators in dynamic seemingly unrelated regression models," Journal of Econometrics, Elsevier, vol. 69(1), pages 241-266, September.
  3. Pesaran, M. H. & Zhao, Z., 1998. "Bias Reduction in Estimating Long-run Relationships from Dynamic Heterogenous Panels," Cambridge Working Papers in Economics 9802, Faculty of Economics, University of Cambridge.
  4. Kiviet, Jan F. & Phillips, Garry D. A., 1994. "Bias assessment and reduction in linear error-correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 215-243, July.
  5. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
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Cited by:
  1. G. Everaert & L. Pozzi, 2004. "Bootstrap Based Bias Correction for Homogeneous Dynamic²² Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/263, Ghent University, Faculty of Economics and Business Administration.
  2. Alexander Chudik & M. Hashem Pesaran, 2013. "Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors," CESifo Working Paper Series 4232, CESifo Group Munich.
  3. Ilse Ruyssen & Gerdie Everaert & Glenn Rayp, 2014. "Determinants and dynamics of migration to OECD countries in a three-dimensional panel framework," Empirical Economics, Springer, vol. 46(1), pages 175-197, February.
  4. Giovanni S.F. Bruno & Anna M. Falzoni & Rodolfo Helg, 2004. "Measuring the effect of globalization on labour demand elasticity: An empirical application to OECD countries," KITeS Working Papers 153, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Feb 2004.
  5. Everaert, Gerdie & Pozzi, Lorenzo, 2007. "Bootstrap-based bias correction for dynamic panels," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1160-1184, April.
  6. G. Everaert, 2009. "Using Backward Means to Eliminate Individual Effects from Dynamic Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/553, Ghent University, Faculty of Economics and Business Administration.

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