Estimation of spatial panel data models with randomly missing data in the dependent variable

Abstract

We suggest and compare different methods for estimating spatial autoregressive panel models with randomly missing data in the dependent variable. We start with a random effects model and then generalize the model by introducing the spatial Mundlak approach. A nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that the generalized nonlinear least squares, best generalized two-stage least squares with imputation, and best method of moments estimators have identical asymptotic variances. The robustness of these estimation methods against unknown heteroscedasticity is also stressed since the traditional maximum likelihood approach yields inconsistent estimates under unknown heteroscedasticity. We provide finite sample evidence through Monte Carlo experiments.