Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples
针对空间自回归Probit模型，提出一种复合边际似然估计方法，避免高维精度矩阵求逆，使超大样本下的估计成为可能。
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.