Advances in Random Utility Models

Horowitz, Joel and Keane, Michael and Bolduc, Denis and Divakar, Suresh and Geweke, John and Gonul, Fusun and Hajivassiliou, Vassilis and Koppelman, Frank and Matzkin, Rosa and Rossi, Peter and Ruud, Paul (1994): Advances in Random Utility Models. Published in: Marketing Letters , Vol. 5:4, (1994): pp. 311-322.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: multinomial probit, semiparametric estimation, Bayesian estimation, simulation
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M3 - Marketing and Advertising > M31 - Marketing

References

Albert, J.H., and S. Chib. (1993). Bayesian Analysis of Binary and Polychotomous Response Data, Journal of the American Statistical Association, 88,669-686.

Amemiya, T. (1985). Advanced Econometrics. Cambridge, MA: Harvard University Press.

Ben-Akiva, M., and S.R. Lerman. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press.

Börsch-Supan, A., and V.A. Hajivassiliou. (1993). Smooth Unbiased Multivariate Probability Simulators for Maximum Likelihood Estimation of Limited Dependent Variable Models, Journal of Econometrics, 58, 347-368.

Bult, J.R. (1993). Semiparametric versus Parametric Classification Models: An Application to Direct Marketing, Journal of Marketing Research, 30, 380-390.

Bunch, D.S. (1991). Estimability in the Multinomial Probit Model, Transportation Research, 25B, 1-12.

Dansie, B. (1985). Parameter Estimability in the Multinomial Probit Model, Transportation Research, 19B, 526-528.

Das, S. (1991). A Semiparametric Structural Analysis of the Idling of Cement Kilns, Journal of Econometrics, 50, 235-256.

Divakar, S. (1993). Measuring Brand Loyalty with Structural State Dependence Models: An Investigation of Some Issues, Ph.D. dissertation, Department of Marketing, University of Iowa, Iowa City.

Domencich, T.A., and D. McFadden. (1975). Urban Travel Demand: A Behavioral Analysis. Amsterdam: North-Holland.

Fischer, G.W., and D. Nagin. (1981). Random Versus Fixed Coefficient Quantal Choice Models. In C.F. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications. Cambridge, MA: MIT Press, 273-304.

Forinash, C.V., and F.S. Koppelman. (1993). Application and Interpretation of Nested Logit Models of Intercity Mode Choice. Transportation Research Reeord, No. 1413, 98-106.

Gabler, S., F. Laisney, and M. Lechner. (1993). Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation, Journai of Business and Economic Statistics, 11, 61-80.

Geweke, J., M. Keane, and D. Runkle. (1992). Alternative Computational Approaches to Inference in the Multinomial Probit Model, Manuscript, Research Department, Federal Reserve Bank of Minneapolis.

Gönül, F.F., and K. Srinivasan. (1993). Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Metbodological and Managerial Issues, Marketing Science, 12,213-229.

Goodwin, B.K. (1993). Semiparametric (Distribution-Free) Testing of Speculative Efficiency in a Parimutuel Gambling Market, Manuscript, North Carolina State University.

Han, A.K. (1987). Non-Parametric Analysis of a Generalized Regression Model, Journal of Econometrics, 35,303-316.

Hajivassiliou, V.A. (1993a). Simulation Estimation Methods for Limited Dependent Variable Models. In G.S. Maddala, C.R. Rao, and H.D. Vinod (eds.), Handbook ofStatistics, Vol. 11. Amsterdam: Elsevier Science Publishers, 519-543.

Hajivassiliou, V.A. (1993b). Simulating Normal Rectangle Probabilities and Their Derivatives: The Effects of Vectorization, International Journal of Supercomputer Applications, 7, 231-253.

Hajivassiliou, V.A., and D. McFadden. (1992). The Method of Simulated Scores for the Estimation of LDV Models, Manuscript, Cowles Foundation for Research in Economics, YMe University, New Haven, CT.

Hajivassiliou, V.A., D. McFadden, and P. Ruud. (1992). Simulation of Multivariate Normal Orthant Probabilities: Methods and Programs, Cowles Foundatiõn Discussion Paper No. 1021, Yale University, New Haven, CT.

Härdle, W. (1990). Applied Nonparametric Regression. New York: Cambridge University Press.

Hausman, J.A., and D.A. Wise. (1978). A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing lnterdependence and Heterogeneous Preferences, Econometrica, 46,403-426.

Hensher, D.A., and L.W. Johnson. (1981). Applied Discrete-Choice Modeling. London: Croom-Helm.

Heckman, J.J. (1981a). The Incidental Parameters Problem and the Problem of Initial Conditions in Estimating a Discrete Time-Discrete Data Stochastic Process. In C.F. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications. Cambridge, MA: MIT Press, 179-195.

Heckman, J.J. (1981b). Statistical Models for Discrete Panel Data. In C.F. Manski and D. McFadden (eds.), Structaral Analysis of Discrete Data with Econometric Applications. Cambridge, MA: MIT Press, 114-178.

Horowitz, J.L. (1993a). Semipärametric Estimation of a Work-Trip Mode Choice Model, Journal of Econometrics, 58, 49-70.

Horowitz, J.L. (1993b). Semiparametric and Nonparametric Estimation of Quantal Response Models. In G.S. Maddala, C.R. Rao, and H.D. Vinod (eds.), Handbook ofStatistics, Vol. 11. Amsterdam: Elsevier Science Publishers, 45-72.

Horowitz, J.L. (1992). A Smoothed Maximum Score Estimator for the Binary Response Model, Econometrica, 60,505-531.

Horowitz, J.L., and W. Härdle. (1992). Testing a Parametric Model Against a Semiparametric Alternative, Econometric Theory, forthcoming.

Horowitz, J.L., and J.J. Louviere. (1993). Testing Predicted Choices Against Observations in Probabilistic Discrete-Choice Models, Marketing Science, 12,270-279.

lchimura, H. (1993). Semiparametric Least Squares (SLS) and Weighted SLS Estimation of Single-Index Models, Journal of Econometrics, 58, 71-120.

Keane, M.P. (1994). A Computationally Practical Simulation Estimator for Panel Data, Econometrica, 62, 95-116.

Klein, R.L., and R.H. Spady. (1993). An Efficient Semiparametric Estimator for Discrete Choice Models, Econometrica, 61,387-422.

Lerman, S.R., and C.F. Manski. (1981). On the Use of Simulated Frequencies to Approximate Choice Probabilities. In C.F. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Eeonometric Applications. Cambridge, MA: MIT Press, 305-319.

Manski, C.F. (1975). Maximum Score Estimation of the Stochastic Utility Model of Choice, Journal of Econometrics, 3,205-228.

Manski, C.F. (1985). Semiparametric analysis of Discrete Response: Asymptotic Properties of the Maximum Score Estimator, Journal of Econometrics, 27, 313-334.

Matzkin, R.L. (1992). Nonparametric and distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models, Econometrica, 60, 239-270.

McCulloch, R., and P.E. Rossi. (1992). An Exact Likelihood Analysis of the Multinomial Probit Model, Journal of Econometrics, forthcoming.

McFadden, D. (1989). A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration, Econometrica, 57,995-1026.

McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (ed.), Frontiers in Econometrics. New York: Academic Press, 105-142.

McFadden, D. (1981). Econometric Models of Probabilistic Choice. In C.F. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications. Cambridge, MA: MIT Press, 198-272.

Pakes, A., and D. Pollard. (1989). Simulation and the Asymptotics of Optimization Estimators, Econometrica, 57, 1027-1058.

Powell, J.L, J.H. Stock, and T.M. Stoker. (1989). Semiparametric Estimation of Index Coefficients, Econometrica, 57, 1403-1430.

Sherman, R.P. (1993). The Limiting Distribution of the Maximum Rank Correlation Estimator, Econometrica, 61, 123-138.

Stern, S. (1992). A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models, Econometrica, 60,943-952.