Spatial Dynamic Panel Data Models with Correlated Random Effects

In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions into sums of terms uncorrelated given idiosyncratic errors, a hybrid method is developed for consistently estimating the variance-covariance matrix of the M-estimators, which again depends only on the process starting time. Monte Carlo results demonstrate that the proposed methods perform well in finite sample.