Missing(s) in Space:
Monte Carlo Simulations and a Bayesian Approach to Missing Data in
Spatial Econometric Models
Using Monte-Carlo simulations and an empirical application from the field of political science, this paper addresses the outlined problem by means of a Bayesian framework. It can be demonstrated how the amount of bias introduced in parameter estimates is almost independent of the nature of the missing process, although it is marginally lower in the case of missings completely at random. Additionally, the amount of bias generally increases with the strength of the underlying spatial association across all different specifications. Finally, allowing for the simultaneous imputation of missing values and model estimation, the pursued Bayesian approach – although computationally intensive – offers an adequate framework to address this bias introduced in models with spatially lagged variables (and/or errors).