Marra, G;
Radice, R;
(2013)
Estimation of a regression spline sample selection model.
Computational Statistics & Data Analysis
, 61
158 - 173.
10.1016/j.csda.2012.12.010.
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Abstract
It is often the case that an outcome of interest is observed for a restricted non-randomly selected sample of the population. In such a situation, standard statistical analysis yields biased results. This issue can be addressed using sample selection models which are based on the estimation of two regressions: a binary selection equation determining whether a particular statistical unit will be available in the outcome equation. Classic sample selection models assume a priori that continuous regressors have a pre-specified linear or non-linear relationship to the outcome, which can lead to erroneous conclusions. In the case of continuous response, methods in which covariate effects are modeled flexibly have been previously proposed, the most recent being based on a Bayesian Markov chain Monte Carlo approach. A frequentist counterpart which has the advantage of being computationally fast is introduced. The proposed algorithm is based on the penalized likelihood estimation framework. The construction of confidence intervals is also discussed. The empirical properties of the existing and proposed methods are studied through a simulation study. The approaches are finally illustrated by analyzing data from the RAND Health Insurance Experiment on annual health expenditures.
Type: | Article |
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Title: | Estimation of a regression spline sample selection model |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.csda.2012.12.010 |
Publisher version: | http://dx.doi.org/10.1016/j.csda.2012.12.010 |
Language: | English |
Additional information: | This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Non-random sample selection, Penalized regression spline, Selection bias, Simultaneous equation system |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1392959 |
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