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Estimation of Factor Structured Covariance Mixed Logit Models

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  • Jonathan James

    (Department of Economics, California Polytechnic State University)

Abstract

Mixed logit models with normally distributed random coefficients are typically estimated under the extreme assumptions that either the random coefficients are completely independent or fully correlated. A factor structured covariance provides a middle ground between these two assumptions. However, because these models are more difficult to estimate, they are not frequently used to model preference heterogeneity. This paper develops a simple expectation maximization algorithm for estimating mixed logit models when preferences are generated from a factor structured covariance. The algorithm is easy to implement for both exploratory and confirmatory factor models. The estimator is applied to stated-preference survey data from residential energy customers (Train, 2007). Comparing the fit across five different models, which differed in their assumptions on the covariance of preferences, the results show that all three factor specifications produced a better fit of the data than the fully correlated model measured by BIC and two out of three performed better in terms of AIC.

Suggested Citation

  • Jonathan James, 2018. "Estimation of Factor Structured Covariance Mixed Logit Models," Working Papers 1802, California Polytechnic State University, Department of Economics.
    • Handle: RePEc:cpl:wpaper:1802
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    References listed on IDEAS

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    1. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
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    3. Jonathan James, 2017. "MM Algorithm for General Mixed Multinomial Logit Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 841-857, June.
    4. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
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    6. Elrod, Terry & Keane, Michael, 1995. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data," MPRA Paper 52434, University Library of Munich, Germany.
    7. Chandra R. Bhat, 1997. "An Endogenous Segmentation Mode Choice Model with an Application to Intercity Travel," Transportation Science, INFORMS, vol. 31(1), pages 34-48, February.
    8. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
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    Cited by:

    1. Coote, Leonard V. & Swait, Joffre & Adamowicz, Wiktor, 2021. "Separating generalizable from source-specific preference heterogeneity in the fusion of revealed and stated preferences," Journal of choice modelling, Elsevier, vol. 40(C).
    2. Youssef M Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2020. "Sparse Covariance Estimation in Logit Mixture Models," Papers 2001.05034, arXiv.org.
    3. Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021. "Discrete Choice Analysis with Machine Learning Capabilities," Papers 2101.10261, arXiv.org.

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    More about this item

    Keywords

    Discrete Choice; Mixed Logit; EM Algorithm; Factor Models;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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