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Improving GMM Efficiency in Dynamic Models for Panel Data with Mean Stationarity

  • Giorgio Calzolari
  • Laura MagazziniEmail author
Chapter
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Estimation of dynamic panel data models largely relies on the generalized method of moments (GMM), and adopted sets of moment conditions exploit information up to the second moment of the variables. However, in many microeconomic applications, the variables of interest are skewed (typical examples are individual wages, size of the firms, number of employees, etc.); therefore, third moments might provide useful information for the estimation process. In this paper, we propose a moment condition, to be added to the set of conditions customarily exploited in GMM estimation of dynamic panel data models, that exploits third moments. The moment condition we propose is based on the data generating process that, under mean stationarity, characterizes the initial observation \(y_{i0}\) and the long-run mean of the dependent variable. In the literature on dynamic panel data models and in the way how Monte Carlo simulations are implemented therein for mean stationary processes, this condition is always fulfilled, but never explicitly exploited for estimation. Monte Carlo experiments show remarkable efficiency improvements when the distribution of individual effects, and thus of \(y_{i0}\), is indeed skewed.

Notes

Acknowledgements

We gratefully acknowledge comments and suggestions from two anonymous Reviewers, Francesca Mantese, and conference participants at the Fifth and Sixth Italian Congress of Econometrics and Empirical Economics.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of StatisticsComputer Science, Applications, Università di FirenzeFirenzeItaly
  2. 2.Department of EconomicsUniversità di VeronaVeronaItaly

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