QML Estimation of Dynamic Panel Data Models with Spatial Errors
Abstract
We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when this assumption is not met.
This paper has been withdrawn.