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Full-information Bayesian Estimation of Cross-sectional Sample Selection Models

In: The Econometrics of Networks

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  • Sophia Ding
  • Peter H. Egger

Abstract

This chapter proposes an approach toward the estimation of cross-sectional sample selection models, where the shocks on the units of observation feature some interdependence through spatial or network autocorrelation. In particular, this chapter improves on prior Bayesian work on this subject by proposing a modified approach toward sampling the multivariate-truncated, cross-sectionally dependent latent variable of the selection equation. This chapter outlines the model and implementation approach and provides simulation results documenting the better performance of the proposed approach relative to existing ones.

Suggested Citation

  • Sophia Ding & Peter H. Egger, 2020. "Full-information Bayesian Estimation of Cross-sectional Sample Selection Models," Advances in Econometrics, in: The Econometrics of Networks, volume 42, pages 205-234, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320200000042013
    DOI: 10.1108/S0731-905320200000042013
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    More about this item

    Keywords

    Sample selection models; spatial econometrics; network econometrics; Bayesian econometrics; cross-section models; full information estimators; C11; C31; C34;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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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