IDEAS home Printed from https://ideas.repec.org/p/lea/leawpi/0308.html
   My bibliography  Save this paper

Panel Binary Variables and Sufficiency: Generalizing Conditional Logit

Author

Listed:
  • Thierry Magnac

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.

Suggested Citation

  • Thierry Magnac, 2003. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit," Research Unit Working Papers 0308, Laboratoire d'Economie Appliquee, INRA.
  • Handle: RePEc:lea:leawpi:0308
    as

    Download full text from publisher

    File URL: http://www.inra.fr/Internet/Departements/ESR/UR/lea/documents/wp/wp0308.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2021. "Sufficient statistics for unobserved heterogeneity in structural dynamic logit models," Journal of Econometrics, Elsevier, vol. 223(2), pages 280-311.
    2. 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.
    3. 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.
    4. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    5. 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.
    6. 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.
    7. Terhi Maczulskij, 2015. "High education and public sector employment: Evidence from Finland using data on twins," Working Papers 296, Palkansaajien tutkimuslaitos, Labour Institute for Economic Research.
    8. Terhi Maczulskij, 2015. "Who chooses to become a public sector employee?," Working Papers 301, Palkansaajien tutkimuslaitos, Labour Institute for Economic Research.
    9. 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.
    10. Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2021. "Integrated likelihood based inference for nonlinear panel data models with unobserved effects," Journal of Econometrics, Elsevier, vol. 223(1), pages 73-95.
    11. Timothy Halliday, 2006. "Identifying State Dependence in Non-Stationary Processes," Working Papers 200601, University of Hawaii at Manoa, Department of Economics.
    12. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2017. "Exponential class of dynamic binary choice panel data models with fixed effects," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 898-927, October.
    13. Maczulskij, Terhi & Viinikainen, Jutta, 2021. "Personality and Public Sector Employment," ETLA Working Papers 86, The Research Institute of the Finnish Economy.
    14. 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.
    15. Timothy Halliday, 2007. "Testing for State Dependence with Time-Variant Transition Probabilities," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 685-703.
    16. 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.
    17. Emir Malikov & Diego A. Restrepo-Tobón & Subal C. Kumbhakar, 2018. "Heterogeneous credit union production technologies with endogenous switching and correlated effects," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1095-1119, November.
    18. Kang, Munsu & Schwab, Benjamin & Yu, Jisang, 2020. "Gender differences in the relationship between land ownership and managerial rights: Implications for intrahousehold farm labor allocation," World Development, Elsevier, vol. 125(C).
    19. Lechner, Michael & Lollivier, Stefan & Magnac, Thierry, 2005. "Parametric Binary Choice Models," IDEI Working Papers 398, Institut d'Économie Industrielle (IDEI), Toulouse.
    20. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," Department of Economics Working Papers 2020-09, McMaster University.
    21. Mogens Fosgerau, 2014. "Nonparametric approaches to describing heterogeneity," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 11, pages 257-267, Edward Elgar Publishing.
    22. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Identification of Time-Varying Transformation Models with Fixed Effects, with an Application to Unobserved Heterogeneity in Resource Shares," Papers 2008.05507, arXiv.org, revised Apr 2021.
    23. repec:spo:wpecon:info:hdl:2441/eu4vqp9ompqllr09ij4j0h0h1 is not listed on IDEAS
    24. 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

    Keywords

    Binary models; panel data; conditional logit; sufficiency;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:lea:leawpi:0308. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.inra.fr/Internet/Departements/ESR/UR/lea/index.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Madeleine Roux The email address of this maintainer does not seem to be valid anymore. Please ask Madeleine Roux to update the entry or send us the correct address (email available below). General contact details of provider: http://www.inra.fr/Internet/Departements/ESR/UR/lea/index.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.