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    Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity

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    Author
    Ando, T
    Date
    2021
    Source Title
    Journal of Econometrics
    Publisher
    Elsevier
    University of Melbourne Author/s
    Ando, Tomohiro
    Affiliation
    Business Administration
    Metadata
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    Document Type
    Journal Article
    Citations
    Ando, T. (2021). Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity. Journal of Econometrics, Forthcoming
    Access Status
    This item is embargoed and will be available on 2023-04-01
    URI
    http://hdl.handle.net/11343/258637
    Abstract
    This paper considers the estimation and inference procedures for the case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed and the model is capable of capturing high-dimensional cross-section dependence. The proposed model also allows for heterogeneous regression coefficients. New Bayesian and non-Bayesian approaches are introduced to estimate the model parameters. We investigate the asymptotic behaviors of the estimated parameters. We show the consistency and asymptotic normality of the estimated regression coefficients and the estimated interactive fixed effects when both the cross-section and time-series dimensions of the panel go to infinity. We prove that the dimensionality of the interactive effects can be consistently estimated by the proposed information criterion. Monte Carlo simulations demonstrate the satisfactory performance of the proposed method. Finally, the method is applied to study the performance of New York City medallion drivers in terms of efficiency.

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