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
Date Written: July 5, 2016
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
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