Partial maximum likelihood estimation of spatial probit models

Honglin Wang (), Emma Iglesias and Jeffrey Wooldridge

Journal of Econometrics, 2013, vol. 172, issue 1, 77-89

Abstract: This paper analyzes spatial Probit models for cross sectional dependent data in a binary choice context. Observations are divided by pairwise groups and bivariate normal distributions are specified within each group. Partial maximum likelihood estimators are introduced and they are shown to be consistent and asymptotically normal under some regularity conditions. Consistent covariance matrix estimators are also provided. Estimates of average partial effects can also be obtained once we characterize the conditional distribution of the latent error. Finally, a simulation study shows the advantages of our new estimation procedure in this setting. Our proposed partial maximum likelihood estimators are shown to be more efficient than the generalized method of moments counterparts.

Keywords: Spatial statistics; Maximum likelihood; Probit model (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 C24 C25 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:172:y:2013:i:1:p:77-89

DOI: 10.1016/j.jeconom.2012.08.005

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