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Advances in Random Utility Models

Author

Listed:
  • Horowitz, Joel
  • Keane, Michael
  • Bolduc, Denis
  • Divakar, Suresh
  • Geweke, John
  • Gonul, Fosun
  • Hajivassiliou, Vassilis
  • Koppelman, Frank
  • Matzkin, Rosa
  • Rossi, Peter
  • Ruud, Paul

Abstract

In recent years, major advances have taken place in three areas of random utility modeling: (1) semiparametric estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian stimation. This paper summarizes these developments and discusses their implications for practice.

Suggested Citation

  • Horowitz, Joel & Keane, Michael & Bolduc, Denis & Divakar, Suresh & Geweke, John & Gonul, Fosun & Hajivassiliou, Vassilis & Koppelman, Frank & Matzkin, Rosa & Rossi, Peter & Ruud, Paul, 1994. "Advances in Random Utility Models," MPRA Paper 53026, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:53026
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    References listed on IDEAS

    as
    1. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    2. 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.
    3. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    4. Joel L. Horowitz & Jordan J. Louviere, 1993. "Testing Predicted Choices Against Observations in Probabilistic Discrete-Choice Models," Marketing Science, INFORMS, vol. 12(3), pages 270-279.
    5. Sherman, Robert P, 1993. "The Limiting Distribution of the Maximum Rank Correlation Estimator," Econometrica, Econometric Society, vol. 61(1), pages 123-137, January.
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    7. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    8. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    9. Das, Sanghamitra, 1991. "A semiparametric structural analysis of the idling of cement kilns," Journal of Econometrics, Elsevier, vol. 50(3), pages 235-256, December.
    10. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    11. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
    12. Dansie, B. R., 1985. "Parameter estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 19(6), pages 526-528, December.
    13. Gabler, Siegfried & Laisney, Francois & Lechner, Michael, 1993. "Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 61-80, January.
    14. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    15. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    16. Vassilis Argyrou Hajivassiliou, 1993. "Simulating Normal Rectangle Probabilities and Their Derivatives: The Effects of Vectorization," Working Papers _025, Yale University.
    17. Horowitz, Joel L., 1993. "Semiparametric estimation of a work-trip mode choice model," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 49-70, July.
    18. Stern, Steven, 1992. "A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models," Econometrica, Econometric Society, vol. 60(4), pages 943-952, July.
    19. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Harry Joe, 2000. "Inequalities for Random Utility Models, with Applications to Ranking and Subset Choice Data," Methodology and Computing in Applied Probability, Springer, vol. 2(4), pages 359-372, December.
    2. Fridah Chepchirchir & Beatrice W. Muriithi & Jackson Langat & Samira A. Mohamed & Shepard Ndlela & Fathiya M. Khamis, 2021. "Knowledge, Attitude, and Practices on Tomato Leaf Miner, Tuta absoluta on Tomato and Potential Demand for Integrated Pest Management among Smallholder Farmers in Kenya and Uganda," Agriculture, MDPI, vol. 11(12), pages 1-20, December.
    3. Wang, Weiren & Zhou, Mai, 1995. "Iterative Least Squares Estimator of Binary Choice Models: a Semi-Parametric Approach," MPRA Paper 46981, University Library of Munich, Germany.
    4. Bartels, Knut & Boztuæg, Yasemin & Müller, Marlene, 1999. "Testing the multinomial logit model," SFB 373 Discussion Papers 1999,19, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Kontogianni, A. & Tourkolias, Ch. & Skourtos, M. & Damigos, D., 2014. "Planning globally, protesting locally: Patterns in community perceptions towards the installation of wind farms," Renewable Energy, Elsevier, vol. 66(C), pages 170-177.

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    More about this item

    Keywords

    multinomial probit; semiparametric estimation; Bayesian estimation; simulation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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