Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity

Abstract

We consider the estimation and inference of fixed effects (FE) spatial dynamic panel data (SDPD) models under small T and unknown heteroskedasticity by extending the M-estimation strategy for homoskedastic FE-SDPD model of Yang (2018, Journal of Econometrics). Unbiased estimating equations are obtained by adjusting the conditional quasi-score functions given the initial observations, leading to M-estimators that are free from the initial conditions and robust against unknown cross-sectional heteroskedasticity. Consistency and asymptotic normality of the proposed M-estimator are established. The standard errors are obtained by representing the estimating equations as sums of martingale differences. Monte Carlo results show that the proposed M-estimators have good finite sample performance. The practical importance and relevance of allowing for heteroskedasticity in the model is illustrated using data on sovereign risk spillover.