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A dynamic model for binary panel data with unobserved heterogeneity admitting a Vn-consistent conditional estimator

Author

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  • Francesco Bartolucci†

    () (Dipartimento di Economia, Finanza e Statistica, Universit`a di Perugia.)

  • Valentina Nigro

    () (Dipartimento di Studi Economico-Finanziari e Metodi Quantitativi, Universit`a di Roma “Tor Vergata”)

Abstract

A model for binary panel data is introduced which allows for state dependence and unobserved heterogeneity beyond the effect of strictly exogenous covariates. The model is of quadratic exponential type and its structure closely resembles that of the dynamic logit model. An economic interpretation of its assumptions, based on expectation about future outcomes, is provided. The main advantage of the proposed model, with respect to the dynamic logit model, is that each individual-specific parameter for the unobserved heterogeneity may be eliminated by conditioning on the sum of the corresponding response variables. A conditional likelihood results which allows us to identify the structural parameters of the model with at least three observations (included an initial observation assumed to be exogenous), even in the presence of time dummies. A pn-consistent conditional estimator of these parameters also results which is very simple to compute. Its finite sample properties are studied by means of a simulation study. Extensions of the proposed approach are discussed with reference, in particular, to the case of more elaborated structures for the state dependence and to that of categorical response variables with more than two levels.

Suggested Citation

  • 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.
  • Handle: RePEc:rtv:ceisrp:97
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    File URL: ftp://www.ceistorvergata.it/repec/rpaper/No-97.pdf
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Thierry Magnac, 2004. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit," Econometrica, Econometric Society, vol. 72(6), pages 1859-1876, November.
    4. 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.
    5. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    6. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296 Elsevier.
    7. Manski, Charles F, 1987. "Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data," Econometrica, Econometric Society, vol. 55(2), pages 357-362, March.
    8. 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.
    9. 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.
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    Cited by:

    1. Schreiber, Sven & Beblo, Miriam, 2016. "Leisure and housing consumption after retirement: New evidence on the life-cycle hypothesis," Discussion Papers 2016/8, Free University Berlin, School of Business & Economics.
    2. 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.

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