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Panel Binary Variables and Sufficiency: Generalizing Conditional Logit

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  • Thierry Magnac

Abstract

This paper extends the conditional logit approach (Rasch, Andersen, Chamberlain) used in panel data models of binary variables with correlated fixed effects and strictly exogenous regressors. In a two-period two-state model, necessary and sufficient conditions on the joint distribution function of the individual-and-period specific shocks are given such that the sum of individual binary variables across time is a sufficient statistic for the individual effect. By extending a result of Chamberlain, it is shown that root-n consistent regular estimators can be constructed in panel binary models if and only if the property of sufficiency holds. In applied work, the estimation method amounts to quasi-differencing the binary variables as if they were continuous variables and transforming a panel data model into a cross-section model. Semiparametric approaches can then be readily applied. Copyright The Econometric Society 2004.

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  • Thierry Magnac, 2004. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit," Econometrica, Econometric Society, vol. 72(6), pages 1859-1876, November.
  • Handle: RePEc:ecm:emetrp:v:72:y:2004:i:6:p:1859-1876
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    File URL: http://hdl.handle.net/10.1111/j.1468-0262.2004.00556.x
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    Cited by:

    1. Février, Philippe & Wilner, Lionel, 2016. "Do consumers correctly expect price reductions? Testing dynamic behavior," International Journal of Industrial Organization, Elsevier, vol. 44(C), pages 25-40.
    2. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.
    3. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    4. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," Review of Economic Studies, Oxford University Press, vol. 82(3), pages 991-1030.
    5. Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Schim van der Loeff, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," CIRANO Working Papers 2007s-06, CIRANO.
    6. Albrecht, James & van den Berg, Gerard J & Vroman, Susan, 2004. "The knowledge lift: The Swedish adult education program that aimed to eliminate low worker skill levels," Working Paper Series 2004:17, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    7. Timothy Halliday, 2006. "Identifying State Dependence in Non-Stationary Processes," Working Papers 200601, University of Hawaii at Manoa, Department of Economics.
    8. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2012. "An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects," CESifo Working Paper Series 4033, CESifo Group Munich.
    9. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.
    10. Timothy Halliday, 2007. "Testing for State Dependence with Time-Variant Transition Probabilities," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 685-703.
    11. Hyytinen, Ari & Pajarinen, Mika, 2008. "Opacity of young businesses: Evidence from rating disagreements," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1234-1241, July.
    12. Lechner, Michael & Lollivier, Stefan & Magnac, Thierry, 2005. "Parametric Binary Choice Models," IDEI Working Papers 398, Institut d'Économie Industrielle (IDEI), Toulouse.
    13. Mogens Fosgerau, 2014. "Nonparametric approaches to describing heterogeneity," Chapters,in: Handbook of Choice Modelling, chapter 11, pages 257-267 Edward Elgar Publishing.
    14. Malikov, Emir & Restrepo-Tobon, Diego A & Kumbhakar, Subal C., 2016. "Heterogeneous Credit Union Production Technologies with Endogenous Switching and Correlated Effects," MPRA Paper 71593, University Library of Munich, Germany.
    15. repec:spo:wpecon:info:hdl:2441/eu4vqp9ompqllr09ij4j0h0h1 is not listed on IDEAS
    16. Francesco Bartolucci† & Valentina Nigro, 2007. "A dynamic model for binary panel data with unobserved heterogeneity admitting a Vn-consistent conditional estimator," CEIS Research Paper 97, Tor Vergata University, CEIS.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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