Research Repository

Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study

Calabrese, Raffaella and Elkink, Johan A (2014) 'Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study.' Journal of Regional Science, 54 (4). pp. 664-687. ISSN 0022-4146

[img]
Preview
Text
1302.3414v1.pdf - Submitted Version

Download (466kB) | Preview

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, followed by an empirical application. 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 performs best for low spatial autocorrelation, while the recursive importance sampler performs best for high spatial autocorrelation. The same results are obtained by increasing the sample size. Finally, the linearized general method of moments estimator is the fastest algorithm that provides accurate estimates for low spatial autocorrelation and large sample size.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Users 161 not found.
Date Deposited: 24 Oct 2014 14:52
Last Modified: 13 Nov 2015 16:33
URI: http://repository.essex.ac.uk/id/eprint/11177

Actions (login required)

View Item View Item