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Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time

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  • Geert Mesters

    ()
    (Netherlands Institute for the Study of Crime and Law Enforcement, and VU University Amsterdam)

  • Siem Jan Koopman

    (VU University Amsterdam)

Abstract

This paper has been accepted for publication in the Journal of Econometrics An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-Gaussian dynamic panel data model with unobserved random individual-specific and time-varying effects. We propose an estimation procedure based on the importance sampling technique. In particular, a sequence of conditional importance densities is derived which integrates out all random effects from the joint distribution of endogenous variables. We disentangle the integration over both the cross-section and the time series dimensions. The estimation method facilitates the flexible modeling of large panels in both dimensions. We evaluate the method in a Monte Carlo study for dynamic panel data models with observations from the Student's t distribution. We finally present an extensive empirical study into the interrelationships between the economic growth figures of countries listed in the Penn World Tables. It is shown that our dynamic panel data model can provide an insightful analysis of common and heterogeneous features in world-wide economic growth.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-009/4.

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Date of creation: 06 Feb 2012
Date of revision: 18 Mar 2014
Handle: RePEc:dgr:uvatin:20120009

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Web page: http://www.tinbergen.nl

Related research

Keywords: Panel data; Non-Gaussian; Importance sampling; Random effects; Student's t; Economic growth;

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References

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  1. Pesaran, M. Hashem, 2004. "A Pair-Wise Approach to Testing for Output and Growth Convergence," IZA Discussion Papers 1313, Institute for the Study of Labor (IZA).
  2. Enrique Moral-Benito, 2007. "Determinants Of Economic Growth: A Bayesian Panel Data Approach," Working Papers, CEMFI wp2007_0719, CEMFI.
  3. Caselli, Francesco & Esquivel, Gerardo & Lefort, Fernando, 1996. " Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics," Journal of Economic Growth, Springer, Springer, vol. 1(3), pages 363-89, September.
  4. Keane, Michael P. & Sauer, Robert M., 2006. "Classification Error in Dynamic Discrete Choice Models: Implications for Female Labor Supply Behavior," IZA Discussion Papers 2332, Institute for the Study of Labor (IZA).
  5. Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2011. "Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models," Tinbergen Institute Discussion Papers, Tinbergen Institute 11-057/4, Tinbergen Institute, revised 27 Jan 2012.
  6. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.
  7. Fruhwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2008. "Model-Based Clustering of Multiple Time Series," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 78-89, January.
  8. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, Econometric Society, vol. 62(1), pages 95-116, January.
  9. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, Elsevier, vol. 149(1), pages 2-11, April.
  10. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, Econometric Society, vol. 67(6), pages 1255-1294, November.
  11. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521522717.
  12. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Papers, University of Pittsburgh, Department of Economics 321, University of Pittsburgh, Department of Economics, revised Jan 2007.
  13. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198523543, October.
  14. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568 Elsevier.
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Cited by:
  1. Siem Jan Koopman & Geert Mesters, 2014. "Empirical Bayes Methods for Dynamic Factor Models," Tinbergen Institute Discussion Papers, Tinbergen Institute 14-061/III, Tinbergen Institute.
  2. Geert Mesters & Victor van der Geest & and Catrien Bijleveld, 2014. "Crime, Employment and Social Welfare: an Individual-level Study on Disadvantaged Males," Tinbergen Institute Discussion Papers, Tinbergen Institute 14-091/III, Tinbergen Institute.

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