Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study

TL;DR

This paper critically compares various estimators for binary spatial autoregressive models through a comprehensive Monte Carlo simulation, highlighting their performance across different levels of spatial autocorrelation and sample sizes.

Contribution

First to provide an extensive Monte Carlo study evaluating the properties of estimators for spatial binary choice models.

Findings

Gibbs estimator performs best at low spatial autocorrelation

Recursive Importance Sampler excels at high spatial autocorrelation

Linearized GMM estimator is fastest and accurate for large samples and low autocorrelation

Abstract

The goal of this paper is to provide a cohesive description and a critical comparison of the main estimators proposed in the literature for spatial binary choice models. The properties of such estimators are investigated using a theoretical and simulation study. To the authors' knowledge, this is the first paper that provides a comprehensive Monte Carlo study of the estimators' properties. This simulation study shows that the Gibbs estimator Le Sage (2000) performs best for low spatial autocorrelation, while the Recursive Importance Sampler Beron & Vijverberg (2004) performs best for high spatial autocorrelation. The same results are obtained by increasing the sample size. Finally, the linearized General Method of Moments estimator Klier & McMillen (2008) is the fastest algorithm that provides accurate estimates for low spatial autocorrelation and large sample size.