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Masking Identification of Discrete Choice Models under Simulation Methods

Author

Listed:
  • Lesley Chiou

    () (Department of Economics, Occidental College)

  • Joan Walker

    () (Center for Transportation Studies, Boston University)

Abstract

We present examples based on actual and synthetic datasets to illustrate how simulation methods can mask identification problems in the estimation of discrete choice models such as mixed logit. Simulation methods approximate an integral (without a closed form) by taking draws from the underlying distribution of the random variable of integration. Our examples reveal how a low number of draws can generate estimates that appear identified, but in fact, are either not theoretically identified by the model or not empirically identified by the data. For the particular case of maximum simulated likelihood estimation, we investigate the underlying source of the problem by focusing on the shape of the simulated log-likelihood function under different conditions.

Suggested Citation

  • Lesley Chiou & Joan Walker, 2005. "Masking Identification of Discrete Choice Models under Simulation Methods," Occidental Economics Working Papers 5, Occidental College, Department of Economics, revised May 2006.
  • Handle: RePEc:occ:wpaper:5
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    File URL: http://faculty.oxy.edu/lchiou/simuldraws.pdf
    File Function: Revised version, 2006
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    References listed on IDEAS

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    1. Stephane Hess & John Polak, 2003. "An alternative method to the scrambled Halton sequence for removing correlation between standard Halton sequences in high dimensions," ERSA conference papers ersa03p406, European Regional Science Association.
    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. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665 National Bureau of Economic Research, Inc.
    4. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    5. 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.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    7. Austan Goolsbee & Amil Petrin, 2004. "The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV," Econometrica, Econometric Society, vol. 72(2), pages 351-381, March.
    8. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    9. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
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    Cited by:

    1. Joan Walker & Jieping Li, 2007. "Latent lifestyle preferences and household location decisions," Journal of Geographical Systems, Springer, vol. 9(1), pages 77-101, April.

    More about this item

    Keywords

    simulation methods; discrete choice;

    JEL classification:

    • 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

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