Analysis of discrete dependent variable models with spatial correlation

Roman Liesenfeld, Jean-Francois Richard and Jan Vogler

No 2013-01, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

Abstract: In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is driven by a latent stochastic state variable which is specified as a linear spatial regression model. The likelihood is a high-dimensional integral whose dimension depends on the sample size. For its evaluation we propose to use efficient importance sampling (EIS). The specific spatial EIS implementation we develop exploits the sparsity of the precision (or covariance) matrix of the errors in the reduced-form state equation typically encountered in spatial settings, which keeps numerically accurate EIS likelihood evaluation computationally feasible even for large sample sizes. The proposed ML approach based upon spatial EIS is illustrated with 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 (search for similar items in EconPapers)
JEL-codes: C15 C21 C25 D22 R12 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-ore and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cauewp:201301

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