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

Listed author(s):
  • Geert Mesters

    ()

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

  • Siem Jan Koopman

    (VU University Amsterdam)

This paper resulted in a publication in the Journal of Econometrics (2014). Volume 180, pages 127-140. 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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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:tin:wpaper:20120009
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