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Nonparametric identification in nonseparable panel data models with generalized fixed effects

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  • Hoderlein, Stefan
  • White, Halbert

Abstract

This paper extends the familiar notion of fixed effects to nonlinear structures with infinite-dimensional unobservables, like preferences. The main result is that a generalized version of differencing identifies local average responses (LARs) in nonseparable structures. In contrast to existing results, this does not require either substantial restrictions on functional form or independence between the persistent unobservables and the explanatory variables of interest, and it requires only two time periods. On the other hand, the results are confined to the subpopulation of “stayers” (Chamberlain, 1982), i.e., the population for which the explanatory variables do not change over time. We extend the basic framework to include time trends and dynamics in the explanatory variables, and we show how distributional effects as well as average partial effects are identified. Our approach also allows endogeneity in the transitory unobservables. Furthermore, we show that this new identification principle can be applied to well-known objects like the slope coefficient in the semiparametric panel data binary choice model with fixed effects. Finally, we suggest estimators for the local average response and average partial effect, and we analyze their large- and finite-sample behavior.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 168 (2012)
Issue (Month): 2 ()
Pages: 300-314

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Handle: RePEc:eee:econom:v:168:y:2012:i:2:p:300-314

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Nonseparable models; Identification; Panel data; Semiparametric; Binary choice;

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Citations

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Cited by:
  1. Xavier D'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear Difference-in-Differences in Repeated Cross Sections with Continuous Treatments," Boston College Working Papers in Economics 839, Boston College Department of Economics.
  2. Longhi, C. & Musolesi, A. & Baumont, C., 2013. "Modeling the industrial dynamics of the European metropolitan areas during the process of economic integration: a semiparametric approach," Working Papers, Grenoble Applied Economics Laboratory (GAEL) 2013-10, Grenoble Applied Economics Laboratory (GAEL).
  3. Sylvie Charlot & Riccardo Crescenzi & Antonio Musolesi, 2014. "Augmented and Unconstrained: revisiting the Regional Knowledge Production Function," SEEDS Working Papers 2414, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2014.
  4. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
  5. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
  6. Mazzanti, M. & Musolesi, A., 2013. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Working Papers, Grenoble Applied Economics Laboratory (GAEL) 2013-08, Grenoble Applied Economics Laboratory (GAEL).
  7. Shiu, Ji-Liang & Hu, Yingyao, 2013. "Identification and estimation of nonlinear dynamic panel data models with unobserved covariates," Journal of Econometrics, Elsevier, Elsevier, vol. 175(2), pages 116-131.
  8. Tomasz Czekaj & Arne Henningsen, 2013. "Panel Data Specifications in Nonparametric Kernel Regression: An Application to Production Functions," IFRO Working Paper 2013/5, University of Copenhagen, Department of Food and Resource Economics.
  9. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  10. Cizek, P. & Lei, J., 2013. "Identification and Estimation of Nonseparable Single-Index Models in Panel Data with Correlated Random Effects," Discussion Paper, Tilburg University, Center for Economic Research 2013-062, Tilburg University, Center for Economic Research.
  11. Stefan Hoderlein & Yuya Sasaki, 2011. "On the role of time in nonseparable panel data models," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP15/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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