Estimation of Spatial Sample Selection Models: A Partial Maximum Likelihood Approach

CentER Discussion Paper Series No. 2016-013

78 Pages Posted: 7 Apr 2016

See all articles by Renata Rabovic

Renata Rabovic

Tilburg University - Center for Economic Research (CentER)

Pavel Cizek

Tilburg University - Department of Econometrics & Operations Research

Date Written: March 31, 2016

Abstract

To analyze data obtained by non-random sampling in the presence of cross-sectional dependence, estimation of a sample selection model with a spatial lag of a latent dependent variable or a spatial error in both the selection and outcome equations is considered. Since there is no estimation framework for the spatial lag model and the existing estimators for the spatial error model are either computationally demanding or have poor small sample properties, we suggest to estimate these models by the partial maximum likelihood estimator, following Wang, et al. (2013)'s framework for a spatial error probit model. We show that the estimator is consistent and asymptotically normally distributed. To facilitate easy and precise estimation of the variance matrix without requiring the spatial stationarity of errors, we propose the parametric bootstrap method. Monte Carlo simulations demonstrate the advantages of the estimators.

Keywords: asymptotic distribution, maximum likelihood, near epoch dependence, sample selection model

JEL Classification: C13, C31, C34

Suggested Citation

Rabovic, Renata and Cizek, Pavel, Estimation of Spatial Sample Selection Models: A Partial Maximum Likelihood Approach (March 31, 2016). CentER Discussion Paper Series No. 2016-013. Available at SSRN: https://ssrn.com/abstract=2756508 or http://dx.doi.org/10.2139/ssrn.2756508

Renata Rabovic (Contact Author)

Tilburg University - Center for Economic Research (CentER) ( email )

P.O. Box 90153
Tilburg, 5000 LE
Netherlands

Pavel Cizek

Tilburg University - Department of Econometrics & Operations Research ( email )

Tilburg, 5000 LE
Netherlands

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