Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples

Pavlo Mozharovskyi and Jan Vogler

Economics Letters, 2016, vol. 148, issue C, 87-90

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 (search for similar items in EconPapers)
JEL-codes: C21 C25 C63 C87 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:148:y:2016:i:c:p:87-90

DOI: 10.1016/j.econlet.2016.09.022

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