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

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. •We implement Composite Marginal Likelihood (CML) for spatial probit models.•Existing CML implementations are infeasible in large samples.•We achieve computational feasibility using sparse matrix techniques.•We illustrate feasibility of our CML implementation through a Monte-Carlo study.