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Root-N Consistent Semiparametric Estimators of a Dynamic Panel Sample Selection Model

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  • George-Levi Gayle
  • Christelle Viauroux

Abstract

This paper considers the problem of identification and estimation in panel-data sample-selection models with a binary selection rule when the latent equations contain possibly predetermined variables, lags of the dependent variables, and unobserved individual effects. The selection equation contains lags of the dependent variables from both the latent and the selection equations as well as other possibly predetermined variables relative to the latent equations. We derive a set of conditional moment restrictions that are then exploited to construct a three-step sieve estimator for the parameters of the main equation including a nonparametric estimator of the sample-selection term. In the second step the unknown parameters of the selection equation are consistently estimated using a transformation approach in the spirit of Berkson's minimum chi-square sieve method and a first-step kernel estimator for the selection probability. This second-step estimator is of interest in its own right. It can be used to semiparametrically estimate a panel-data binary response model with correlated random effects without making any distributional assumptions. We show that both estimators (second and third stage) are √n-consistent and asymptotically normal.This paper considers the problem of identification and estimation in panel-data sample-selection models with a binary selection rule when the latent equations contain possibly predetermined variables, lags of the dependent variables, and unobserved individual effects. The selection equation contains lags of the dependent variables from both the latent and the selection equations as well as other possibly predetermined variables relative to the latent equations. We derive a set of conditional moment restrictions that are then exploited to construct a three-step sieve estimator for the parameters of the main equation including a nonparametric estimator of the sample-selection term. In the second step the unknown parameters of the selection equation are consistently estimated using a transformation approach in the spirit of Berkson's minimum chi-square sieve method and a first-step kernel estimator for the selection probability. This second-step estimator is of interest in its own right. It can be used to semiparametrically estimate a panel-data binary response model with a nonparametric individual specific effect without making any other distributional assumptions. We show that both estimators (second and third stage) are √n-consistent and asymptotically normal.

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Paper provided by Carnegie Mellon University, Tepper School of Business in its series GSIA Working Papers with number 2004-E62.

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Handle: RePEc:cmu:gsiawp:1095622259

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Postal: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890
Web page: http://www.tepper.cmu.edu/

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Cited by:
  1. Maria Teresa Costa-Campi & Néstor Duch-Brown & José García-Quevedo, 2013. "R&D drivers and obstacles to innovation in the energy industry," Working Papers 2013/23, Institut d'Economia de Barcelona (IEB).
  2. Giulia Bettin & Riccardo Lucchetti, 2012. "Intertemporal remittance behaviour by immigrants in Germany," Mo.Fi.R. Working Papers 75, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
  3. Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Schim van der Loeff, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," CESifo Working Paper Series 1992, CESifo Group Munich.
  4. Spiess, Martin & Kroh, Martin, 2010. "A Selection Model for Panel Data: The Prospects of Green Party Support," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 172-188.
  5. Soiliou Namoro & Wayne-Roy Gayle, 2006. "Estimation of a Nonlinear Panel Data Model with Predetermined Variables and Semiparametric Individual Effects," Working Papers 251, University of Pittsburgh, Department of Economics, revised Sep 2008.
  6. Gayle, Wayne-Roy & Namoro, Soiliou Daw, 2013. "Estimation of a nonlinear panel data model with semiparametric individual effects," Journal of Econometrics, Elsevier, vol. 175(1), pages 46-59.
  7. Georg-Levi Gayle & Limor Golan & Mehmet A. Soytas, . "Estimating the Returns to Parental Time Investment in Children Using a Life Cycle Dynastic Model," GSIA Working Papers 2011-E18, Carnegie Mellon University, Tepper School of Business.

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