Advertisement

Parametric Binary Choice Models

  • Michael Lechner
  • Stéfan Lollivier
  • Thierry Magnac
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 46)

Keywords

Panel Data Probit Model Panel Data Model Simulated Maximum Likelihood Binary Choice Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Altonji, J.G., and L.M., Segal, 1996, “Small-sample bias in GMM estimation of covariance structures”, Journal of Business Economics and Statistics, 14: 353–366.CrossRefGoogle Scholar
  2. Andersen, E.B., 1970, “Asymptotic properties of conditional maximum likelihood estimators”, Journal of the Royal Statistic Society, Series B, 32: 283–301.Google Scholar
  3. Angrist, J.D., 2001, “Estimation of limited dependent variable models with dummy endogenous regressors: Simple strategies for empirical practice”, Journal of Business Economics and Statistics, 19:2–16.CrossRefGoogle Scholar
  4. Arellano, M., 2003, “Discrete choice with panel data”, Investigaciones Economicas, 27, 423–458.Google Scholar
  5. Arellano, M., and R., Carrasco, 2003, “Binary choice panel data Models with Predetermined Variables”, Journal of Econometrics, 115, 125–157.CrossRefGoogle Scholar
  6. Arellano, M., and B., Honoré, 2001, “Panel data models: Some recent developments”, in J. Heckman and E. Leamer (Eds.), Handbook of Econometrics, North Holland: Amsterdam V(53):3229–3296.Google Scholar
  7. Avery, R.B, L.P., Hansen, and V.J., Hotz, 1983, “Multiperiod probit models and orthogonality condition estimation”, International Economic Review, 24:21–35.CrossRefGoogle Scholar
  8. Baltagi, B.H., 2000, Econometric Analysis of Panel Data, Wiley: London.Google Scholar
  9. Butler, J., and R., Moffitt, 1982, “A computationally efficient quadrature procedure for the one-factor multinomial probit model ”, Econometrica, 50(3): 761–764.CrossRefGoogle Scholar
  10. Bertschek, I., and M., Lechner, 1998, “Convenient estimators for the panel probit model”, Journal of Econometrics, 87: 329–371.CrossRefGoogle Scholar
  11. Blundell, R., R., Griffith, and F., Windmeijer, 2002, “Individual Effects and Dynamics in Count data Models”, Journal of Econometrics, 108: 113–131.CrossRefGoogle Scholar
  12. Breitung, J., and M., Lechner, 1997, “Some GMM estimation methods and specification tests for nonlinear models”, in L. Mátyás and P.Sevestre (Eds.), The Econometrics of Panel Data, 2nd ed., Dordrecht: Kluwer, 583–612, 1996.Google Scholar
  13. Carro, J.M., 2003, “Estimating dynamic panel data discrete choice models with fixed effects”, Working paper, CEMFI, 0304.Google Scholar
  14. Chamberlain, G., 1980, “Analysis of covariance with qualitative data”, Review of Economic Studies, 47: 225–238.CrossRefGoogle Scholar
  15. Chamberlain, G., 1984, “Panel Data”, in Z. Griliches and M.D. Intrilligator (Eds.), Handbook of Econometrics, vol II, ch 22, Elsevier Science: Amsterdam, 1248–1318.Google Scholar
  16. Chamberlain, G., 1985, “Heterogeneity, omitted variable bias and duration dependence”, in Longitudinal Analysis of Labor Market Data, in J.J. Heckman and B. Singer (Eds.), Cambridge UP: Cambridge.Google Scholar
  17. Chamberlain, G., 1992, “Binary response models for panel data: Identification and information”, Mimeo, Harvard University: Cambridge.Google Scholar
  18. Charlier, E., B., Melenberg, and A., van Soest, 1995, “A smoothed maximum score estimator for the binary choice panel model and an application to labour force participation”, Statistica Neerlandica, 49: 324–342.CrossRefGoogle Scholar
  19. Chen, S., 1998, “Root-N consistent estimation of a panel data sample selection model”, unpublished manuscript, Hong Kong university.Google Scholar
  20. Chib, S., 2001, “Markov Chain Monte Carlo Methods: Computation and Inference”, in J. Heckman and E. Leamer (Eds.), Handbook of Econometrics, V(57):3570–3649.Google Scholar
  21. Chib, S., and E., Greenberg, 1998, “Analysis of multivariate probit models”, Biometrika, 85:347–61.CrossRefGoogle Scholar
  22. Chib, S., and I., Jeliazkov, 2002, “Semiparametric hierarchical bayes analysis of discrete panel data with state dependence”, Washington University, working paper.Google Scholar
  23. Cox, D.R., and M., Reid, 1987, “Parameter orthogonality and approximate conditional inference”, Journal of the Royal Statistical Society, Series B, 49:1–39.Google Scholar
  24. Crépon, B., and J., Mairesse, 1996, “The chamberlain approach to panel data: An overview and some simulation experiments”, in L. Matyas and P. Sevestre (Eds.), The Econometrics of Panel Data, Kluwer: Amsterdam.Google Scholar
  25. Geweke, J., M., Keane, and D.E., Runkle, 1997, “Statistical inference in the multinomial multiperiod probit model”, Journal of Econometrics, 80, 125–165.CrossRefGoogle Scholar
  26. Geweke, J.F., and M., Keane, 2001, “Computationally intensive methods for integration in econometrics”, in J. Heckman and E. Leamer (Eds.), Handbook of Econometrics, V(56):3465–3568.Google Scholar
  27. Gouriéroux, C., and A., Monfort, 1993, “Simulation-based inference: A survey with special reference to panel data models”, Journal of Econometrics, 59: 5–33.CrossRefGoogle Scholar
  28. Gouriéroux, C., and A., Monfort, 1996, Simulation-based Econometric Methods, Louvain: CORE Lecture Series.Google Scholar
  29. Gouriéroux, C., A., Monfort, and A., Trognon, 1984, “Pseudo-likelihood methods - Theory”, Econometrica, 52: 681–700.CrossRefGoogle Scholar
  30. Greene, W., 2002, “The Bias of the fixed effects estimator in non linear models”, New York University: New York, unpublished manuscript.Google Scholar
  31. Greene, W., 2003, Econometric Analysis, 5th ed., Prentice Hall: Englewood Cliffs.Google Scholar
  32. Guilkey, D.K., and Murphy, J.L., 1993, “Estimation and testing in the random effects probit model”, Journal of Econometrics, 59: 301–317.CrossRefGoogle Scholar
  33. Hahn, J., and G., Kuersteiner, 2004, “Bias reduction for dynamic nonlinear panel models with fixed effects”, MIT unpublished manuscript.Google Scholar
  34. Hahn, J., and W., Newey, 2004, “Jackknife and analytical Bias reduction for nonlinear panel data models”, Econometrica, 72:1295–1319.CrossRefGoogle Scholar
  35. Hajivassiliou, V., and D., McFadden, 1998, “The method of simulated scores for the estimation of LDV models ”, Econometrica, 66: 863–896.CrossRefGoogle Scholar
  36. Hajivassiliou, V., D., McFadden, and P., Ruud, 1996, “Simulation of multivariate normal rectangle probabilities and their derivatives. Theorical and computational results”, Journal of Econometrics, 72: 85–134.CrossRefGoogle Scholar
  37. Heckman, J.J., 1981a, “The incidental parameters problem and the problem of initial conditions in estimating a discrete time – discrete data stochastic process and some Monte-Carlo evidence,” in C. Manski and D. McFadden (Eds.), Structural Analysis of Discrete Data, MIT Press, Cambridge, MA, 179–195.Google Scholar
  38. Heckman, J.J., 1981b, “Statistical models for discrete panel Data” in C. Manski and D. McFadden (Eds.), Structural Analysis of Discrete Data, MIT Press, Cambridge, MA, 114:178.Google Scholar
  39. Heckman, J.J., and B., Singer, 1984, “A method for minimizing the impact of distributional assumptions in econometric models for duration data”, Econometrica, 52:271–320.CrossRefGoogle Scholar
  40. Honoré, B., 2002, “Non-linear models with panel data”, WP CEMMAP, 13/02.Google Scholar
  41. Honoré, B., and E., Kyriazidou, 2000, “Panel data discrete choice modles with lagged dependent variables”, Econometrica, 68:839–874.CrossRefGoogle Scholar
  42. Honoré, B.E., and A., Lewbel, 2002, “Semiparametric binary choice panel data models without strict exogeneity”, Econometrica, 70:2053–2063.CrossRefGoogle Scholar
  43. Horowitz, J., 1992, “A smoothed maximum score estimator for the binary response model”, Econometrica, 60: 505–531.CrossRefGoogle Scholar
  44. Hsiao, C., 1992, “Logit and Probit Models”, in L. Mátyás and P. Sevestre (Eds.), The Econometrics of Panel Data: Handbook of Theory and Applications, Chap. 11: 223–241, Kluwer:Amsterdam.Google Scholar
  45. Hsiao, C., 1996, “Logit and probit models”, in L. Mátyás and P. Sevestre (Eds.), The Econometrics of Panel Data: Handbook of Theory and Applications, 2nd ed., Chap. 16: 410–428, Kluwer: Amsterdam.Google Scholar
  46. Hsiao, C., 2003, Analysis of panal data, 2nd ed., Cambridge University Press, Econometric Society Monographs, 11.Google Scholar
  47. Inkman, J., 2000, “Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators”, Journal of Econometrics, 97: 227–259.CrossRefGoogle Scholar
  48. Kamionka, T., 1998, “Simulated maximum likelihood estimation in transition models”, Econometrics Journal, 1:C129–153.CrossRefGoogle Scholar
  49. Keane, M.P., 1994, “A computationally efficient practical simulation estimator for panel data”, Econometrica, 62:95–116.CrossRefGoogle Scholar
  50. Kim, J., and D., Pollard, 1990, “Cube root asymptotics”, Annals of Statistics, 18: 191–219.CrossRefGoogle Scholar
  51. Kyriazidou, E., 1995, Essays in Estimation and Testing of Econometric Models, Ph.D. dissertation, Northwestern University.Google Scholar
  52. Laisney, F., and M., Lechner, 2002, “Almost consistent estimation of panel probit models with ‘Small’ fixed effects”, Discussion paper no. 2002–15, University of St. Gallen.Google Scholar
  53. Lancaster, A., 2000, “The incidental parameter problem since 1948”, Journal of Econometrics, 95:391–413.CrossRefGoogle Scholar
  54. Lancaster, A., 2003, An Introduction to Modern Bayesian Econometrics, Blackwell: Oxford.Google Scholar
  55. Lechner, M., 1993, “Estimation of limited dependent variable habit persistence models on panel data with an application to the dynamics of self-employment in the former east germany”, in H. Bunzel, P. Jensen, and N. Westergå rd-Nielson, (Eds.), Panel Data and Labour Market Dynamics,: North-Holland Amsterdam, 263–283.Google Scholar
  56. Lechner, M., 1995, “Some specification tests for probit models estimated on panel data”, Journal of Business & Economic Statistics, 13: 475–488, 1995.CrossRefGoogle Scholar
  57. Lee, L.F., 1992, “On efficiency of methods of simulated moments and maximum simulated likelihood estimation of discrete response models”, Econometric Theory, 8:518–552.Google Scholar
  58. Lee, L.F., 1995, “Asymptotic bias in simulated maximum likelihood estimation of discrete choice models”, Econometric Theory, 11:437–483.Google Scholar
  59. Lee, L.F., 1997, “Simulated maximum likelihood estimation of dynamic discrete choice statistical models: Some Monte carlo results”, Journal of Econometrics, 82:1–35.CrossRefGoogle Scholar
  60. Lee, L.F., 2000, “A numerically stable quadrature procedure for the one-factor random component discrete choice model”, Journal of Econometrics, 95: 117–129.CrossRefGoogle Scholar
  61. Lee, M.J., 1999, “A root-n consistent semiparametric estimator for related-effect binary response panel data”, Econometrica, 67:427–33.CrossRefGoogle Scholar
  62. Lee, M.J., 2002, Panel Data Econometrics, Academic Press: New York.CrossRefGoogle Scholar
  63. Lewbel, A., 2000, “Semiparametric qualitative response model estimation with unknown Heteroskedasticity or instrumental variables”, Journal of Econometrics, 97:145–177.CrossRefGoogle Scholar
  64. McFadden, D., 1989, “A method of simulated moments for estimation od discrete response models without numerical integration”, Econometrica, 57: 995–1026.CrossRefGoogle Scholar
  65. Magnac, T., 2000, “State dependence and unobserved heterogeneity in youth employment histories”, The Economic Journal, 110:805–837CrossRefGoogle Scholar
  66. Magnac, T. 2004, “Binary Variables and Sufficiency: Generalizing the Conditional Logit”, Econometrica, 72:1859–1876.CrossRefGoogle Scholar
  67. Manski, C.F. 1975, “Maximum Score Estimation of the Stochastic Utility Model”, Journal of Econometrics, 3, 205–228.CrossRefGoogle Scholar
  68. Manski, C.F., 1987, “Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data”, Econometrica, 55, 357–362.CrossRefGoogle Scholar
  69. Montalvo, J.G., 1997, “GMM estimation of count-panel-data models with Fixed Effects and Predetermined Instruments”, Journal of Business Economics and Statistics, 15: 82–89.CrossRefGoogle Scholar
  70. Mroz, T., 1999, “Discrete factor approximation in simultaneous equation models: Estimating the impact of a dummy endogenous variable on continuous outcome”, Journal of Econometrics,92: 233–274.CrossRefGoogle Scholar
  71. Mundlak, Y., 1978, “On the Pooling of Time Series and Cross Section Data”, Econometrica, 46: 69–85.CrossRefGoogle Scholar
  72. Newey, W., 1993, “Efficient estimation of models with conditional moment restrictions”, in G.S. Maddala, C. Rao, Vinod, H. (Eds.), Handbook of Statistics, Vol. 11, Ch. 16, North-Holland: Amsterdam.Google Scholar
  73. Newey, W., 1994, “The asymptotic variance of semiparametric estimators”, Econometrica, 62:1349–1382.CrossRefGoogle Scholar
  74. Newey, W.K., and McFadden, D., 1994, “Large sample estimation and hypothesis testing”, in R.F. Engle and D.L. McFadden (eds.), Handbook of Econometrics, Vol. 4, 2113–2245, North-Holland: Amsterdam.Google Scholar
  75. Pagan, A., and A., Ullah, 1998, Nonparametric Econometrics, Cambridge UP, Cambridge.Google Scholar
  76. Robinson, P.M., 1982, “On the asymptotic properties of estimators of models containing limited dependent variables”, Econometrica, 50:27–41.CrossRefGoogle Scholar
  77. Sevestre, P., 2002, Econométrie des données de panel, Dunod: Paris.Google Scholar
  78. Thomas, A., 2003, “Consistent estimation of binary-choice panel data models with heterogeneous linear trends”, LEERNA-INRA Toulouse, unpublished manuscript.Google Scholar
  79. Train, K., 2002, Discrete Choices with Simulation, Cambridge UP:Cambridge.Google Scholar
  80. Wooldridge, J., 2000, Introductory Econometrics, 2nd ed., South-Western College Publishing: Boston, MA.Google Scholar
  81. Wooldridge, J., 2002, “Simple solutions to the initial conditions problem in dynamic non linear panel data models with unobserved heterogeneity”, WP CEMMAP, London, 18/02.Google Scholar
  82. Woutersen T., 2002, “Robustness against incidental parameters”, Western Ontario, unpublished manuscript.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michael Lechner
    • 1
  • Stéfan Lollivier
    • 2
  • Thierry Magnac
    • 3
  1. 1.Swiss Institute for Empirical Economic Research (SEW)University of St. GallenSwitzerland
  2. 2.INSEEFrance
  3. 3.Toulouse School of Economics, Manufacture des TabacsUniversité de Toulouse 1France

Personalised recommendations