GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications

Abstract: We introduce a new estimator, CRE-GMM, which exploits the correlated random effects (CRE) approach within the generalised method of moments (GMM), specifically applied to level equations, GMM-lev. It has the advantage of estimating the effect of measurable time-invariant covariates using all available information. This is not possible with GMM-dif, applied to the equations of each period transformed into first differences, while GMM-sys uses little information as it adds the equation in levels for only one period. The GMM-lev, by implying a two-component error term containing individual heterogeneity and shock, exposes the explanatory variables to possible double endogeneity. For example, the estimation of actual persistence could suffer from bias if instruments were correlated with the unit-specific error component. The CRE-GMM deals with double endogeneity, captures initial conditions and enhance inference. Monte Carlo simulations for different panel types and under different double endogeneity assumptions show the advantage of our approach. The empirical applications on production and R&D contribute to clarify the advantages of using CRE-GMM.