Panel data models with nonadditive unobserved heterogeneity : estimation and inference
Author(s)Lee, Joonhwan; Fernández-Val, Iván
Massachusetts Institute of Technology. Department of Economics.
MetadataShow full item record
This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest - means, variances, and other moments of the random coefficients - are estimated by cross sectional sample moments of GMM estimators applied separately to the time series of each individual. To deal with the incidental parameter problem introduced by the noise of the within-individual estimators in short panels, we develop bias corrections. These corrections are based on higher-order asymptotic expansions of the GMM estimators and produce improved point and interval estimates in moderately long panels. Under asymptotic sequences where the cross sectional and time series dimensions of the panel pass to infinity at the same rate, the uncorrected estimator has an asymptotic bias of the same order as the asymptotic variance. The bias corrections remove the bias without increasing variance. An empirical example on cigarette demand based on Becker, Grossman and Murphy (1994) shows significant heterogeneity in the price effect across U.S. states.
Thesis: S.M., Massachusetts Institute of Technology, Department of Economics, 2014."February 2014." Abstract page contains the following information: "This paper is based in part on the second chapter of Fernández-Val (2005)'s MIT PhD dissertation." -- Authors: "Iván Fernández-Val and Joonhwan Lee." Cataloged from PDF version of thesis.Includes bibliographical references (pages 25-27 (first group)).
DepartmentMassachusetts Institute of Technology. Department of Economics.
Massachusetts Institute of Technology