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Boosting conditional logit model

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  • Shi, Haolun
  • Yin, Guosheng

Abstract

A componentwise smoothing spline-based boosting procedure is developed for the conditional logit model to estimate the covariate effects nonparametrically. The proposed method can be applied to discrete choice modeling to predict the choice outcomes. Our boosting procedure possesses the properties of slow over-fitting behaviour, automatical variable selection, consistent approximation to the utility function, and the ability to capture the potential nonlinear covariate effects. We show in the simulation studies that the method can provide accurate estimates of the true functional forms of the covariate effects and can select the predictors that are most related to the choice utility. The proposed boosting conditional logit procedure is also applied to two real datasets and its prediction accuracy is demonstrated to be superior to that of the conventional conditional logit regression.

Suggested Citation

  • Shi, Haolun & Yin, Guosheng, 2018. "Boosting conditional logit model," Journal of choice modelling, Elsevier, vol. 26(C), pages 48-63.
    • Handle: RePEc:eee:eejocm:v:26:y:2018:i:c:p:48-63
    DOI: 10.1016/j.jocm.2017.07.002
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    1. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-424, March.
    2. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    3. Reid, Stephen & Tibshirani, Rob, 2014. "Regularization Paths for Conditional Logistic Regression: The clogitL1 Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i12).
    4. 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.
    5. Li, Baibing, 2011. "The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 461-473, March.
    6. Hensher, David A. & Ton, Tu T., 2000. "A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(3), pages 155-172, September.
    7. Hausman, Jerry A & Wise, David A, 1978. "A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences," Econometrica, Econometric Society, vol. 46(2), pages 403-426, March.
    8. Huang, J u-Chin & Nychka, Douglas W., 2000. "A nonparametric multiple choice method within the random utility framework," Journal of Econometrics, Elsevier, vol. 97(2), pages 207-225, August.
    9. Börjesson, Maria & Fosgerau, Mogens & Algers, Staffan, 2012. "Catching the tail: Empirical identification of the distribution of the value of travel time," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 378-391.
    10. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    11. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    12. Huang, Ju-Chin & Nychka, Douglas W. & Smith, V. Kerry, 2008. "Semi-parametric discrete choice measures of willingness to pay," Economics Letters, Elsevier, vol. 101(1), pages 91-94, October.
    13. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    14. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    15. Jingfeng Lu & Isabelle Perrigne, 2008. "Estimating risk aversion from ascending and sealed-bid auctions: the case of timber auction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(7), pages 871-896.
    16. Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
    17. Kim, Dong-Hyuk, 2015. "Nonparametric estimation of utility function in first-price sealed-bid auctions," Economics Letters, Elsevier, vol. 126(C), pages 101-106.
    18. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    19. Steckel, Joel H & Vanhonacker, Wilfried R, 1988. "A Heterogeneous Conditional Logit Model of Choice," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 391-398, July.
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    3. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.

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