Journal Article

Non-Parametric Identification and Estimation of Truncated Regression Models

Songnian Chen

in The Review of Economic Studies

Published on behalf of Review of Economic Studies Ltd

Volume 77, issue 1, pages 127-153
Published in print January 2010 | ISSN: 0034-6527
Published online January 2010 | e-ISSN: 1467-937X | DOI: http://dx.doi.org/10.1111/j.1467-937X.2009.00572.x
Non-Parametric Identification and Estimation of Truncated Regression Models

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In this paper, we consider non-parametric identification and estimation of truncated regression models in both cross-sectional and panel data settings. For the cross-sectional case, Lewbel and Linton (2002) considered non-parametric identification and estimation through continuous variation under a log-concavity condition on the error distribution. We obtain non-parametric identification under weaker conditions. In particular, we obtain non-parametric identification through discrete variation under a non-periodicity condition on the hazard function of the error distribution. Furthermore, we show that the presence of continuous regressors may lead to stronger identification results. Our non-parametric estimator is shown to be consistent and asymptotically normal, and outperforms that of Lewbel and Linton (2002) in a simulation study. For the panel data setting, we provide the first systematic treatment of non-parametric identification and estimation of the truncated panel data model with fixed effects by extending our treatment of the cross-sectional case. We also consider various other extensions.

Journal Article.  9777 words.  Illustrated.

Subjects: Economics

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