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Estimation of spatial sample selection models: A partial maximum likelihood approach

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  • Rabovič, Renata
  • Čížek, Pavel

Abstract

We study estimation of sample selection models with the spatially lagged latent dependent variable or spatial errors in both the selection and outcome equations under cross-sectional dependence. Since there is no estimation framework for the spatial-lag model and the existing estimators for the spatial-error model are computationally demanding or have poor small sample properties, we suggest to estimate these models by the partial maximum likelihood estimator. We show that the estimator is consistent and asymptotically normally distributed. To facilitate easy and precise estimation of the variance matrix, we propose the parametric bootstrap method. Simulations demonstrate the advantages of the estimators.

Suggested Citation

  • Rabovič, Renata & Čížek, Pavel, 2023. "Estimation of spatial sample selection models: A partial maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 232(1), pages 214-243.
    • Handle: RePEc:eee:econom:v:232:y:2023:i:1:p:214-243
    DOI: 10.1016/j.jeconom.2021.10.011
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    More about this item

    Keywords

    Asymptotic distribution; Maximum likelihood; Near epoch dependence; Sample selection model; Spatial autoregressive model;
    All these keywords.

    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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