Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/68466 
Year of Publication: 
2013
Series/Report no.: 
Economics Working Paper No. 2013-01
Publisher: 
Kiel University, Department of Economics, Kiel
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.
Subjects: 
Count data models
Discrete choice models
Firm location choice
Importance sampling
Monte Carlo integration
Spatial econometrics
JEL: 
C15
C21
C25
D22
R12
Document Type: 
Working Paper

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