Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables

Roman Liesenfeld, Jean-Francois Richard and Jan Vogler

A chapter in Spatial Econometrics: Qualitative and Limited Dependent Variables, 2016, vol. 37, pp 35-77 from Emerald Group Publishing Limited

Abstract: We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

Keywords: Count data models; discrete choice models; firm location choice; importance sampling; Monte Carlo integration; spatial econometrics; C15; C21; C25; D22; R12 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320160000037009

DOI: 10.1108/S0731-905320160000037009

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