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What can we learn about correlations from multinomial probit estimates?

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Listed:
  • Chiara Monfardini

    (Dipartimento di Scienze Economiche, Università di Bologna)

  • Joao Santos Silva

    (Department of Economics, University of Essex)

Abstract

It is well known that, in a multinomial probit, only the covariance matrix of the location and scale normalized utilities are identified. In this note, we explore the relation between these identifiable parameters and the original elements of the covariance matrix, to find out what can be learnt about the correlations between the stochastic components of the non-normalized utilities.

Suggested Citation

  • Chiara Monfardini & Joao Santos Silva, 2008. "What can we learn about correlations from multinomial probit estimates?," Economics Bulletin, AccessEcon, vol. 3(28), pages 1-9.
    • Handle: RePEc:ebl:ecbull:eb-08c20028
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    References listed on IDEAS

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    1. Dansie, B. R., 1985. "Parameter estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 19(6), pages 526-528, December.
    2. Monfardini, Chiara, 2003. "An illustration of Cox's non-nested testing procedure for logit and probit models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 425-444, March.
    3. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    4. Andrew Chesher & J. M. C. Santos Silva, 2002. "Taste Variation in Discrete Choice Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(1), pages 147-168.
    5. Peter Hansen, 2003. "Asymptotic Tests of Composite Hypotheses," Working Papers 2003-09, Brown University, Department of Economics.
    6. Wolak, Frank A., 1989. "Local and Global Testing of Linear and Nonlinear Inequality Constraints in Nonlinear Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(1), pages 1-35, April.
    7. Joseph A. Herriges & Catherine L. Kling, 1999. "Nonlinear Income Effects in Random Utility Models," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 62-72, February.
    8. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    9. Joel Horowitz, 1981. "Testing the Multinomial Logit Model against the Multinomial Probit Model without Estimating the Probit Parameters," Transportation Science, INFORMS, vol. 15(2), pages 153-163, May.
    10. Wolak, Frank A., 1989. "Testing inequality constraints in linear econometric models," Journal of Econometrics, Elsevier, vol. 41(2), pages 205-235, 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. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
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