A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Autocorrelated Disturbances

Badi H Baltagi,Michael Pfaffermayr,Peter Egger

doi:10.2139/ssrn.1815357

Abstract

This paper examines the consequences of model misspecification using a panel data model with spatially autocorrelated disturbances. The performance of several maximum likelihood estimators assuming different specifications for this model are compared using Monte Carlo experiments. These include (i) MLE of a random effects model that ignore the spatial correlation; (ii) MLE described in Anselin (1988) which assumes that the individual effects are not spatially autocorrelated; (iii) MLE described in Kapoor, et al. (2006) which assumes that both the individual effects and the remainder error are governed by the same spatial autocorrelation; (iv) MLE described in Baltagi, et al. (2006) which allows the spatial correlation parameter for the individual effects to be different from that of the remainder error term. The latter model encompasses the other models and allows the researcher to test these specifications as restrictions on the general model using LM and LR tests. In fact, based on these tests, we suggest a pretest estimator which is shown to perform well in Monte Carlo experiments, ranking a close second to the true MLE in mean squared error performance.

Highlights

The recent literature on spatial panel data models with error components adopts two alternative spatial autoregressive error processes
In our Monte Carlo experiments below, we examine how this a¤ects the corresponding MSE of the pretest estimator
Given that the researcher does not know the true model, this paper suggests a pretest estimator that performed well in Monte Carlo experiments no matter what the true underlying model

Summary

Introduction

The recent literature on spatial panel data models with error components adopts two alternative spatial autoregressive error processes. One speci...cation assumes that only the remainder error term is spatially correlated but the individual e¤ects are not (Anselin, 1988, Baltagi, Song, and Koh, 2003 and Anselin, Le Gallo and Jayet, 2005; we refer to this as the Anselin model). The other speci...cation assumes that both the individual and remainder error components follow the same spatial error process (see Kapoor, Kelejian, and Prucha, 2006; we refer to this as the KKP model). This paper compares the performance of ML-estimates of these models under misspeci...cation and suggests a pretest estimator based on the LM-tests derived by Baltagi, Egger, and Pfa¤ermayr (2006).

Maximum likelihood estimators of the alternative models

Monte Carlo experiments

Conclusions