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Average and Quantile Effects in Nonseparable Panel Models

2013; Wiley; Volume: 81; Issue: 2 Linguagem: Inglês

10.3982/ecta8405

1468-0262

Victor Chernozhukov, Iván Fernández‐Val, Jinyong Hahn, Whitney K. Newey,

Spatial and Panel Data Analysis

EconometricaVolume 81, Issue 2 p. 535-580 Average and Quantile Effects in Nonseparable Panel Models Victor Chernozhukov, Victor Chernozhukov Dept. of Economics, Massachusetts Institute of Technology, Cambridge, MA 02142, U.S.A.; [email protected]Search for more papers by this authorIván Fernández-Val, Iván Fernández-Val Dept. of Economics, Boston University, Boston, MA 02215, U.S.A.; [email protected]Search for more papers by this authorJinyong Hahn, Jinyong Hahn Dept. of Economics, University of California, Los Angeles, Los Angeles, CA 90095, U.S.A.; [email protected]Search for more papers by this authorWhitney Newey, Whitney Newey Dept. of Economics, Massachusetts Institute of Technology, Cambridge, MA 02142, U.S.A.; [email protected] We thank J. Angrist, G. Chamberlain, D. Chetverikov, B. Frandsen, B. Graham, J. Hausman, and many seminar participants for comments. Brad Larsen and Seongyeon Chang provided capable research assistance. Parts of this paper were given at the 2007 CEMMAP Microeconometrics: Measurement Matters Conference, the Shanghai Lecture of the 2010 World Congress of the Econometric Society, and conferences in between. We gratefully acknowledge research support from the NSF.Search for more papers by this author Victor Chernozhukov, Victor Chernozhukov Dept. of Economics, Massachusetts Institute of Technology, Cambridge, MA 02142, U.S.A.; [email protected]Search for more papers by this authorIván Fernández-Val, Iván Fernández-Val Dept. of Economics, Boston University, Boston, MA 02215, U.S.A.; [email protected]Search for more papers by this authorJinyong Hahn, Jinyong Hahn Dept. of Economics, University of California, Los Angeles, Los Angeles, CA 90095, U.S.A.; [email protected]Search for more papers by this authorWhitney Newey, Whitney Newey Dept. of Economics, Massachusetts Institute of Technology, Cambridge, MA 02142, U.S.A.; [email protected] We thank J. Angrist, G. Chamberlain, D. Chetverikov, B. Frandsen, B. Graham, J. Hausman, and many seminar participants for comments. Brad Larsen and Seongyeon Chang provided capable research assistance. Parts of this paper were given at the 2007 CEMMAP Microeconometrics: Measurement Matters Conference, the Shanghai Lecture of the 2010 World Congress of the Econometric Society, and conferences in between. We gratefully acknowledge research support from the NSF.Search for more papers by this author First published: 20 March 2013 https://doi.org/10.3982/ECTA8405Citations: 119 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time-homogeneity conditions that are like "time is randomly assigned" or "time is an instrument." Partial-identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial-identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations. Citing Literature Volume81, Issue2March 2013Pages 535-580 RelatedInformation