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

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  • Rabovic, Renata

    (Tilburg University, School of Economics and Management)

  • Cizek, Pavel

    (Tilburg University, School of Economics and Management)

Abstract

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 in the presence of cross-sectional dependence. 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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Rabovic, Renata & Cizek, Pavel, 2016. "Estimation of Spatial Sample Selection Models : A Partial Maximum Likelihood Approach," Other publications TiSEM 8a4b2e5d-6787-4685-8b9e-1, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:8a4b2e5d-6787-4685-8b9e-128d0e6d4e47
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    File URL: https://pure.uvt.nl/ws/portalfiles/portal/11035769/2016_013.pdf
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    References listed on IDEAS

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    Cited by:

    1. Dogan, Osman & Taspinar, Suleyman, 2016. "Bayesian Inference in Spatial Sample Selection Models," MPRA Paper 82829, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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