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Simulated z-tests in multinomial probit models

  • Ziegler, Andreas

Within the framework of Monte Carlo experiments, this paper systematically compares different versions of the simulated z-test (using the GHK simulator) in one- and multiperiod multinomial probit models. One important finding is that, in the flexible probit models, the tests on parameters of explanatory variables mostly provide robust results in contrast to the tests on variance-covariance parameters. Overall, neither the amount of random draws in the GHK simulator nor the choice of a certain version of the simulated z-test have a strong influence on the test results. This finding refers to the conformity between the shares of type I errors and the basic significance levels as well as to the number of type II errors. In contrast, the number of type II errors in the simulated z-tests on variance-covariance parameters is reduced by increasing the sample size. Effects of misspecifications on simulated z-tests only appear in the multiperiod multinomial probit model. In this case, the inclusion of the concept of the quasi maximum likelihood theory in the simulated z-test provides comparatively more favourable results.

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Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 01-53.

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Date of creation: 2001
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Handle: RePEc:zbw:zewdip:5409
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  1. Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.
  2. John F. Geweke & Michael P. Keane & David E. Runkle, 1994. "Statistical inference in the multinomial multiperiod probit model," Staff Report 177, Federal Reserve Bank of Minneapolis.
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  9. Lee, Lung-fei, 1999. "Statistical Inference With Simulated Likelihood Functions," Econometric Theory, Cambridge University Press, vol. 15(03), pages 337-360, June.
  10. Lee, Lung-Fei, 1995. "Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models," Econometric Theory, Cambridge University Press, vol. 11(03), pages 437-483, June.
  11. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
  12. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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  14. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
  15. BOLDUC, Denis & FORTIN, Bernard & GORDON, Stephen, 1995. "Multinomial Probit Estimation of Spatially Interdependent Choices: an Empirical Comparison of Two New Techniques," Cahiers de recherche 9508, Université Laval - Département d'économique.
  16. Bolduc, Denis, 1999. "A practical technique to estimate multinomial probit models in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 33(1), pages 63-79, February.
  17. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
  18. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
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