Estimating Panel Data Models in the Presence of Endogeneity and Selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.

[1]  Ekaterini Kyriazidou,et al.  Estimation of Dynamic Panel Data Sample Selection Models , 2001 .

[2]  Rosa L. Matzkin,et al.  Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors , 2005 .

[3]  M. Verbeek,et al.  Testing for selectivity bias in panel data models , 1992 .

[4]  W. Härdle,et al.  Optimal Smoothing in Single-index Models , 1993 .

[5]  Y. Mundlak.  On the Pooling of Time Series and Cross Section Data , 1978 .

[6]  Arthur Lewbel,et al.  Simple Endogenous Binary Choice and Selection Panel Model Estimators , 2006 .

[7]  Erik Meijer,et al.  The Sample Selection Model from a Method of Moments Perspective , 2007 .

[8]  Bertrand Melenberg,et al.  An analysis of housing expenditure using semiparametric models and panel data , 2001 .

[9]  Jeffrey M. Woodbridge.  Econometric Analysis of Cross Section and Panel Data , 2002 .

[10]  James L. Powell,et al.  Estimation of semiparametric models , 1994 .

[11]  S. Haberman.  Analysis of qualitative data , 1978 .

[12]  Gary Chamberlain,et al.  Analysis of Covariance with Qualitative Data , 1979 .

[13]  M. E. Rochina,et al.  Selection Correction in Panel Data Models: An Application to the Estimation of Females' Wage Equations , 2007 .

[14]  Whitney K. Newey,et al.  Nonparametric Estimation of Sample Selection Models , 2003 .

[15]  B. Baltagi,et al.  Wage policy in the health care sector: a panel data analysis of nurses' labour supply. , 2003, Health economics.

[16]  Ekaterini Kyriazidou,et al.  Estimation of a Panel Data Sample Selection Model , 1997 .

[17]  Stephen G. Donald,et al.  Series estimation of semilinear models , 1994 .

[18]  P. Robinson.  ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION , 1988 .

[19]  María Engracia Rochina-Barrachina,et al.  A New Estimator for Panel Data Sample Selection Models , 1999 .

[20]  W. Newey,et al.  Convergence rates and asymptotic normality for series estimators , 1997 .

[21]  endogenous variables , 1996 .

[22]  Jeffrey M. Wooldridge,et al.  Selection corrections for panel data models under conditional mean independence assumptions , 1995 .

[23]  R. Spady,et al.  AN EFFICIENT SEMIPARAMETRIC ESTIMATOR FOR BINARY RESPONSE MODELS , 1993 .

[24]  Jorge González-Chapela,et al.  On the Price of Recreation Goods as a Determinant of Male Labor Supply , 2007, Journal of Labor Economics.

[25]  W. Newey,et al.  The asymptotic variance of semiparametric estimators , 1994 .

[26]  H. Ichimura,et al.  SEMIPARAMETRIC LEAST SQUARES (SLS) AND WEIGHTED SLS ESTIMATION OF SINGLE-INDEX MODELS , 1993 .