Semiparametric two-stage estimation of sample selection models subject to Tobit-type selection rules
type: Item
instance of: scholarly article
title: Semiparametric two-stage estimation of sample selection models subject to Tobit-type selection rules
zbMATH Open document ID: 0796.62103
DOI: 10.1016/0304-4076(94)90088-4
author: Lung-fei Lee
published in: Journal of Econometrics
publication date: 1994-10-16T00:00:00Z
full work available at URL: http://hdl.handle.net/2027.42/31675
review text: The distributional restrictions of the commonly used econometric models of choice and sample selection are relaxed here in terms of semiparametric methods involving two stages. The model equations are \[ y_ 1 = x_ 1 \alpha_ 0 + u \qquad \text{and}\qquad y_ 2 = x_ 2 \beta_ 0 + v \] where \(\alpha_ 0\) and \(\beta_ 0\) are the true parameter column vectors and the values of \(y_ 1\) and \(y_ 2\) are observable only when they are positive. It is a censored regression model if \(\max\{0,y_ 1\}\) and the corresponding \(x\) are observable. But it is a truncated regression model if the positive values of \(y\), and their corresponding \(x\) are observable. An estimator is proposed for \(\beta_ 0\) and Monte Carlo simulations are performed to compare the performance of this estimator.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62G07
zbMATH DE Number: 549120
zbMATH Keywords: semiparametric estimation
zbMATH Keywords: choice-based models
zbMATH Keywords: sample selection
zbMATH Keywords: censored regression model
zbMATH Keywords: truncated regression model
zbMATH Keywords: Monte Carlo simulations
MaRDI profile type: Publication
OpenAlex ID: W2109494911
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description: scientific article; zbMATH DE number 549120