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Masking Identification of Discrete Choice Models under Simulation Methods

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  • Lesley Chiou

    ()
    (Department of Economics, Occidental College)

  • Joan Walker

    ()
    (Center for Transportation Studies, Boston University)

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    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.

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    Bibliographic Info

    Paper provided by Occidental College, Department of Economics in its series Occidental Economics Working Papers with number 5.

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    Length: 42 pages
    Date of creation: Aug 2005
    Date of revision: May 2006
    Handle: RePEc:occ:wpaper:5

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    Related research

    Keywords: simulation methods; discrete choice;

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    References

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    1. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 35(7), pages 677-693, August.
    2. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, National Bureau of Economic Research, Inc, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc.
    3. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt3tb6j874, University of California Transportation Center.
    4. 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, Elsevier, vol. 58(3), pages 347-368, August.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521747387.
    6. Austan Goolsbee & Amil Petrin, 2004. "The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV," Econometrica, Econometric Society, Econometric Society, vol. 72(2), pages 351-381, 03.
    7. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, Econometric Society, vol. 63(4), pages 841-90, July.
    8. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    9. 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.
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    Cited by:
    1. Joan Walker & Jieping Li, 2007. "Latent lifestyle preferences and household location decisions," Journal of Geographical Systems, Springer, Springer, vol. 9(1), pages 77-101, April.

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