Sample Selection with Binary Endogenous Variable: A Bayesian Analysis of Participation to Timber Auctions

Telecom Paris Economics and Social Sciences Working Paper No. ESS-06-08

31 Pages Posted: 20 Sep 2006

See all articles by Raphaele Preget

Raphaele Preget

French National Institute for Agricultural Research (INRA) - UMR Economie Publique

Patrick Waelbroeck

Telecom ParisTech

Date Written: September 2006

Abstract

We propose a Bayesian Metropolis-Gibbs Monte Carlo Markov Chain (MCMC) algorithm to estimate parameters of a sample selection model in which the selected equation include a binary endogenous explanatory variable, using a three simultaneous equation model. We apply our methodology to participation in timber auctions for which some lots receive no bid (these lots are censored), one bid (no competition) and two or more bids. We find that the MCMC algorithm provides stable results across different model specifications, whereas the Heckman sample selection procedure results in unreliable inference on the coefficient associated with the binary endogenous variable as well as the correlation coefficient.

Keywords: sample selection, binary endogenous explanatory variable, Metropolis

JEL Classification: C11, C15, C34, C35, C63, D44, L73

Suggested Citation

Preget, Raphaele and Waelbroeck, Patrick, Sample Selection with Binary Endogenous Variable: A Bayesian Analysis of Participation to Timber Auctions (September 2006). Telecom Paris Economics and Social Sciences Working Paper No. ESS-06-08. Available at SSRN: https://ssrn.com/abstract=931616 or http://dx.doi.org/10.2139/ssrn.931616

Raphaele Preget (Contact Author)

French National Institute for Agricultural Research (INRA) - UMR Economie Publique ( email )

Centre de Grignon
BP01
Thiverval-Grignon, 78850
France

Patrick Waelbroeck

Telecom ParisTech ( email )

46 rue Barrault
F-75634 Paris, Cedex 13
France

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