Composite Marginal Likelihood Estimation of Spatial Autoregressive Probit Models Feasible in Very Large Samples

9 Pages Posted: 8 Jul 2016 Last revised: 14 Jul 2016

Pavlo Mozharovskyi

Agrocampus Ouest

Jan Vogler

University of Cologne, Department of Economics

Date Written: July 5, 2016

Abstract

Composite Marginal Likelihood (CML) has become a popular approach for estimating spatial probit models. However, for spatial autoregressive specifications the existing brute-force implementations are infeasible in large samples as they rely on inverting the high-dimensional precision matrix of the latent state variable. The contribution of this paper is to provide a CML implementation that circumvents inversion of that matrix and therefore can also be applied to very large sample sizes.

Keywords: Spatial probit models, Sparse matrices, Composite Marginal Likelihood, Partial Maximum Likelihood, Spatial econometrics

JEL Classification: C21, C25

Suggested Citation

Mozharovskyi, Pavlo and Vogler, Jan, Composite Marginal Likelihood Estimation of Spatial Autoregressive Probit Models Feasible in Very Large Samples (July 5, 2016). Available at SSRN: https://ssrn.com/abstract=2806151 or http://dx.doi.org/10.2139/ssrn.2806151

Pavlo Mozharovskyi

Agrocampus Ouest ( email )

65 rue de Saint-Brieuc
Rennes, 35042
France

HOME PAGE: http://https://perso.univ-rennes1.fr/pavlo.mozharovskyi/

Jan Vogler (Contact Author)

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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