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On the role of time in nonseparable panel data models

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  • Stefan Hoderlein

    ()
    (Institute for Fiscal Studies and Boston College)

  • Yuya Sasaki

Abstract

This paper contributes to the understanding of the source of identification in panel data models. Recent research has established that few time periods suffice to identify interesting structural effects in nonseparable panel data models even in the presence of complex correlated unobservables, provided these unobservables are time invariant. A communality of all of these approaches is that they point identify effects only for subpopulations. In this paper we focus on average partial derivatives and continuous explanatory variables. We elaborate on the parallel between time in panels and instrumental variables in cross sections and establish that point identification is generically only possible in specific subpopulations, for finite T . Moreover, for general subpopulations, we provide sharp bounds. Finally, we show that these bounds converge to point identification as T tends to infinity only. We systematize this behavior by comparing it to increasing the number of support points of an instrument. Finally, we apply all of these concepts to the semiparametric panel binary choice model and establish that these issues determine the rates of convergence of estimators for the slope coefficient.

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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP15/11.

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Date of creation: May 2011
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Handle: RePEc:ifs:cemmap:15/11

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  1. Richard Blundell & James Powell, 2001. "Endogeneity in nonparametric and semiparametric regression models," CeMMAP working papers CWP09/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Manuel Arellano & Stéphane Bonhomme, 2009. "Identifying distributional characteristics in random coefficients panel data models," CeMMAP working papers CWP22/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Andrew Chesher, 2003. "Nonparametric identification under discrete variation," CeMMAP working papers CWP19/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Myoung-jae Lee, 1999. "A Root-N Consistent Semiparametric Estimator for Related-Effect Binary Response Panel Data," Econometrica, Econometric Society, vol. 67(2), pages 427-434, March.
  5. Bryan S. Graham & James Powell, 2008. "Identification and Estimation of 'Irregular' Correlated Random Coefficient Models," NBER Working Papers 14469, National Bureau of Economic Research, Inc.
  6. Stefan Hoderlein & Halbert White, 2009. "Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects," Boston College Working Papers in Economics 746, Boston College Department of Economics.
  7. repec:oup:restud:v:79:y::i:3:p:987-1020 is not listed on IDEAS
  8. V. Chernozhukov & Ivan Fernandez-Val, . "Quantile and Average Effects in Nonseparable Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-011, Boston University - Department of Economics.
  9. Bester, C. Alan & Hansen, Christian, 2009. "Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 235-250.
  10. Joseph G. Altonji & Rosa L. Matzkin, 2001. "Panel Data Estimators for Nonseparable Models with Endogenous Regressors," NBER Technical Working Papers 0267, National Bureau of Economic Research, Inc.
  11. Azeem M. Shaikh & Edward J. Vytlacil, 2011. "Partial Identification in Triangular Systems of Equations With Binary Dependent Variables," Econometrica, Econometric Society, vol. 79(3), pages 949-955, 05.
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