To read this content please select one of the options below:

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

ISBN: 978-1-83867-576-9, eISBN: 978-1-83867-575-2

Publication date: 19 October 2020

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.

Keywords

Acknowledgements

Acknowledgments

The authors gratefully acknowledge numerous useful comments from one anonymous reviewer and the editor in charge (Marcel Voia) on an earlier version of the manuscript.

Citation

Ding, S. and Egger, P.H. (2020), "Full-information Bayesian Estimation of Cross-sectional Sample Selection Models", de Paula, Á., Tamer, E. and Voia, M.-C. (Ed.) The Econometrics of Networks (Advances in Econometrics, Vol. 42), Emerald Publishing Limited, Leeds, pp. 205-234. https://doi.org/10.1108/S0731-905320200000042013

Publisher

:

Emerald Publishing Limited