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Panel Binary Variables and Sufficiency: Generalizing Conditional Logit

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  • Thierry Magnac

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Abstract

This paper extends the conditional logit approach 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. Under these conditions, root-n consistent conditional likelihood estimators exist. Moreover, it is shown by extending Chamberlain (1992) that root-n consistent regular estimators can be constructed in panel binary models if and only if the property of sufficiency holds. Imposing sufficiency is shown to reduce the dimensionality of the bivariate distribution function of the individual-and-period specific shocks. This setting is much less restrictive than the conditional logit approach (Rasch, Andersen, Chamberlain). In applied work, it amounts to quasi-difference the binary variables as if they were continuous variables and to transform a panel data model into a cross-section model. Semiparametric approaches can then be readily applied.

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Bibliographic Info

Paper provided by Laboratoire d'Economie Appliquee, INRA in its series Research Unit Working Papers with number 0308.

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Length: 24 pages
Date of creation: Jun 2003
Date of revision:
Handle: RePEc:lea:leawpi:0308

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Keywords: Binary models; panel data; conditional logit; sufficiency;

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Cited by:
  1. Mohnen, Pierre & Raymond, Wladimir & Palm, Franz & Schim van der Loeff, Sybrand, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," MERIT Working Papers 007, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  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. 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.
  4. repec:spo:wpecon:info:hdl:2441/eu4vqp9ompqllr09ij4j0h0h1 is not listed on IDEAS
  5. Timothy Halliday, 2006. "Identifying State Dependence in Non-Stationary Processes," Working Papers 200601, University of Hawaii at Manoa, Department of Economics.
  6. Geert Dhaene & Koen Jochmans, 2014. "Split-Panel Jackknife Estimation of Fixed-Effect Models," Sciences Po publications 2014-03, Sciences Po.
  7. 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.
  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. Timothy Halliday, 2007. "Testing for State Dependence with Time-Variant Transition Probabilities," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 685-703.
  10. 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.
  11. 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, 03.

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