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

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 yi0 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 yi0, is indeed skewed.