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Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects

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Author Info

  • Stefan Hoderlein

    (Boston College)

  • Halbert White

    ()
    (University of California-San Diego)

Abstract

This paper is concerned with extending the familiar notion of fixed effects to nonlinear setups with infinite dimensional unobservables like preferences. The main result is that a generalized version of differencing identifies local average structural derivatives (LASDs) in very general nonseparable models, while allowing for arbitrary dependence between the persistent unobservables and the regressors of interest even if there are only two time periods. These quantities specialize to well known objects like the slope coefficient in the semiparametric panel data binary choice model with fixed effects. We extend the basic framework to include dynamics in the regressors and time trends, and show how distributional effects as well as average effects are identified. In addition, we show how to handle endogeneity in the transitory component. Finally, we adapt our results to the semiparametric binary choice model with correlated coefficients, and establish that average structural marginal probabilities are identified. We conclude this paper by applying the last result to a real world data example. Using the PSID, we analyze the way in which the lending restrictions for mortgages eased between 2000 and 2004.

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Bibliographic Info

Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 746.

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Date of creation: 11 Jul 2009
Date of revision:
Handle: RePEc:boc:bocoec:746

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Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA
Phone: 617-552-3670
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Web page: http://fmwww.bc.edu/EC/
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Related research

Keywords: Nonseparable Models; Identification; Panel Data; Semiparametric; Binary Choice.;

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References

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  1. Susanne Schennach & Halbert White & Karim Chalak, 2007. "Local Indirect Least Squares and Average Marginal Effects in Nonseparable Structural Systems," Boston College Working Papers in Economics 680, Boston College Department of Economics, revised 26 Dec 2009.
  2. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney Newey, 2009. "Identification and estimation of marginal effects in nonlinear panel models," CeMMAP working papers CWP05/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Stefan Hoderlein, 2009. "How many consumers are rational?," CeMMAP working papers CWP32/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Halbert White & Karim Chalak, 2008. "Identifying Structural Effects in Nonseparable Systems Using Covariates," Boston College Working Papers in Economics 734, Boston College Department of Economics.
  5. Manuel Arellano & Stéphane Bonhomme, 2009. "Identifying Distributional Characteristics In Random Coefficients Panel Data Models," Working Papers wp2009_0904, CEMFI.
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Citations

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Cited by:
  1. 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.
  2. Stefan Hoderlein & Yuya Sasaki, 2011. "On the role of time in nonseparable panel data models," CeMMAP working papers CWP15/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  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. Xavier D'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Cizek, P. & Lei, J., 2013. "Identification and Estimation of Nonseparable Single-Index Models in Panel Data with Correlated Random Effects," Discussion Paper 2013-062, Tilburg University, Center for Economic Research.
  7. Shiu, Ji-Liang & Hu, Yingyao, 2013. "Identification and estimation of nonlinear dynamic panel data models with unobserved covariates," Journal of Econometrics, Elsevier, vol. 175(2), pages 116-131.
  8. 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 2013-10, Grenoble Applied Economics Laboratory (GAEL).
  9. Shakeeb Khan & Maria Ponomareva & Elie Tamer, 2011. "Identification of Panel Data Models with Endogenous Censoring," Working Papers 11-07, Duke University, Department of Economics.

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