Identification of Panel Data Models with Endogenous Censoring

42 Pages Posted: 19 May 2011 Last revised: 20 Jun 2011

See all articles by Shakeeb Khan

Shakeeb Khan

Duke University - Department of Economics

Maria Ponomareva

Northwestern University - Department of Economics

Elie T. Tamer

Harvard University

Date Written: May 31, 2011

Abstract

This paper analyzes the identification question in censored panel data models, where the censoring can depend on both observable and unobservable variables in arbitrary ways. Under some general conditions, we derive the tightest sets on the parameter of interest. These sets (which can be singletons) represent the limit of what one can learn about the parameter of interest given the model and the data in that every parameter that belongs to these sets is observationally equivalent to the true parameter. We consider two separate sets of assumptions, motivated by the previous literature, each controlling for unobserved heterogeneity with an individual specific (fixed) effect. The first imposes a stationarity assumption on the unobserved disturbance terms, along the lines of Manski (1987), and Honore (1993). The second is a nonstationary model that imposes a conditional independence assumption. For both models, we provide sufficient conditions for these models to point identify the parameters. Since our identified sets are defined through parameters that obey first order dominance, we outline easily implementable approaches to build confidence regions based on recent advances in Linton et.al.(2010) on bootstrapping tests of stochastic dominance. We also extend our results to dynamic versions of the censored panel models in which we consider lagged observed, latent dependent variables and lagged censoring indicator variables as regressors.

Keywords: panel data, partial identification, endogenous censoring

JEL Classification: C23, C24, C32

Suggested Citation

Khan, Shakeeb and Ponomareva, Maria and Tamer, Elie T., Identification of Panel Data Models with Endogenous Censoring (May 31, 2011). Economic Research Initiatives at Duke (ERID) Working Paper No No. 98, Available at SSRN: https://ssrn.com/abstract=1831402 or http://dx.doi.org/10.2139/ssrn.1831402

Shakeeb Khan (Contact Author)

Duke University - Department of Economics ( email )

213 Social Sciences Building
Box 90097
Durham, NC 27708-0204
United States

Maria Ponomareva

Northwestern University - Department of Economics ( email )

2003 Sheridan Road
Evanston, IL 60208
United States
847-492-0411 (Phone)

Elie T. Tamer

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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