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Semiparametric Binary Choice Panel Data Models without Strictly Exogeneous Regressors

  • Bo E. Honore

    (Princeton University)

  • Arthur Lewbel

    ()

    (Boston College)

Previous estimators of binary choice panel data models with fixed effects require strong parametric error asumptions, strictly exogeneous regressors, or both. This is because nonlinearity of the model precludes the use of the "moment conditions on differences" based estimators that are generally employed for linear models without strictly exogeneous regressors. Based on the cross section binary choice estimator in Lewbel (2000a), we show how discrete choice panel data models with fixed effects can be estimated with only predetermined regressors. The estimator is semiparametric in that the error distribution is not specified, it allows for some general forms of heteroskedasticity, and converges at rate root n.

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Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 455.

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Length: 16 pages
Date of creation: 15 Nov 1998
Date of revision: 22 Sep 2001
Handle: RePEc:boc:bocoec:455
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  1. R Blundell & Steven Bond, . "Initial conditions and moment restrictions in dynamic panel data model," Economics Papers W14&104., Economics Group, Nuffield College, University of Oxford.
  2. Newey, W.K., 1989. "The Asymptotic Variance Of Semiparametric Estimotors," Papers 346, Princeton, Department of Economics - Econometric Research Program.
  3. Adams, Robert M & Berger, Allen N & Sickles, Robin C, 1999. "Semiparametric Approaches to Stochastic Panel Frontiers with Applications in the Banking Industry," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 349-58, July.
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  6. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
  7. Arellano, Manuel & Carrasco, Raquel, 2003. "Binary choice panel data models with predetermined variables," Journal of Econometrics, Elsevier, vol. 115(1), pages 125-157, July.
  8. Arthur Lewbel & Daniel McFadden & Oliver Linton, 1997. "Estimating Features of a Distribution from Binomial Data," Boston College Working Papers in Economics 442, Boston College Department of Economics, revised 01 Jul 2010.
  9. Haerdle,Wolfgang & Stoker,Thomas, 1987. "Investigations smooth multiple regression by the method of average derivatives," Discussion Paper Serie A 107, University of Bonn, Germany.
  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. Heckman, James J & Macurdy, Thomas E, 1980. "A Life Cycle Model of Female Labour Supply," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 47-74, January.
  12. Arthur Lewbel, 2000. "Asymptotic Trimming for Bounded Density Plug-in Estimators," Boston College Working Papers in Economics 479, Boston College Department of Economics, revised 30 Oct 2000.
  13. Manski, Charles F, 1987. "Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data," Econometrica, Econometric Society, vol. 55(2), pages 357-62, March.
  14. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
  15. Sherman, Robert P., 1994. "U-Processes in the Analysis of a Generalized Semiparametric Regression Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 372-395, June.
  16. Eric Maurin, 1999. "The Impact of Parental Income on Early Schooling Transitions : A Re-examination Using Data over Three Generations," Working Papers 99-69, Centre de Recherche en Economie et Statistique.
  17. repec:cup:etheor:v:10:y:1994:i:2:p:372-95 is not listed on IDEAS
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