IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v170y2012i1p102-116.html
   My bibliography  Save this article

Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data

Author

Listed:
  • Bartolucci, Francesco
  • Nigro, Valentina

Abstract

We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter must be distinguished from the state dependence which is accounted for by including the lagged response variable among the regressors. By conditioning on the sufficient statistics, we derive a pseudo conditional likelihood estimator of the structural parameters of the dynamic logit model, which is simple to compute. Asymptotic properties of this estimator are studied in detail. Simulation results show that the estimator is competitive in terms of efficiency with estimators recently proposed in the econometric literature.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:econom:v:170:y:2012:i:1:p:102-116
    DOI: 10.1016/j.jeconom.2012.03.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407612000954
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Gregori Baetschmann & Kevin E. Staub & Rainer Winkelmann, 2015. "Consistent estimation of the fixed effects ordered logit model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 685-703, June.
    3. 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.
    4. Wooldridge, Jeffrey M., 2000. "A framework for estimating dynamic, unobserved effects panel data models with possible feedback to future explanatory variables," Economics Letters, Elsevier, vol. 68(3), pages 245-250, September.
    5. Bo E. Honoré & Elie Tamer, 2006. "Bounds on Parameters in Panel Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 74(3), pages 611-629, May.
    6. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, vol. 67(6), pages 1255-1294, November.
    7. Hahn, Jinyong, 2001. "The Information Bound Of A Dynamic Panel Logit Model With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 17(05), pages 913-932, October.
    8. Thierry Magnac, 2004. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit," Econometrica, Econometric Society, vol. 72(6), pages 1859-1876, November.
    9. Francesco Bartolucci & Fulvia Pennoni, 2007. "On the approximation of the quadratic exponential distribution in a latent variable context," Biometrika, Biometrika Trust, vol. 94(3), pages 745-754.
    10. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    11. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    12. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.
    13. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    14. Hahn, Jinyong & Kuersteiner, Guido, 2011. "Bias Reduction For Dynamic Nonlinear Panel Models With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 27(06), pages 1152-1191, December.
    15. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
    16. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters,in: Studies in Labor Markets, pages 91-140 National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Pauline Givord & Lionel Wilner, 2015. "When Does the Stepping‐Stone Work? Fixed‐Term Contracts Versus Temporary Agency Work in Changing Economic Conditions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 787-805, August.
    2. repec:jss:jstsof:v:079:i08 is not listed on IDEAS
    3. Yoshitsugu Kitazawa, 2017. "DFEL-RTN, a set of TSP programs for root-N consistent estimations of dynamic fixed effects logit models," Discussion Papers 81, Kyushu Sangyo University, Faculty of Economics.
    4. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.
    5. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    6. Lee Myoung-jae, 2015. "Panel conditional and multinomial logit with time-varying parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 317-337, June.
    7. Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    8. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.
    9. repec:pal:jintbs:v:49:y:2018:i:3:d:10.1057_s41267-017-0123-7 is not listed on IDEAS
    10. repec:jss:jstsof:v:078:i07 is not listed on IDEAS
    11. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    12. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    13. Yoshitsugu Kitazawa, 2013. "Exploration of dynamic fixed effects logit models from a traditional angle," Discussion Papers 60, Kyushu Sangyo University, Faculty of Economics.

    More about this item

    Keywords

    Log-linear models; Longitudinal data; Pseudo likelihood inference; Quadratic exponential distribution;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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

    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:eee:econom:v:170:y:2012:i:1:p:102-116. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

    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.