AbstractThis paper presents the application of a new algorithm for maximizing the simulated likelihood functions appearing in the estimation of mixed multinomial logit (MMNL) models. The method uses Monte Carlo sampling to produce the approximate likelihood function and dynamically adapts the number of draws on the basis of statistical estimators of the simulation error and simulation bias. Its convergence from distant starting points is ensured by a trust-region technique, in which improvement is ensured by locally maximizing a quadratic model of the objective function. Simulated data are first used to assess the quality of the results obtained and the relative performance of several algorithmic variants. These variants involve, in particular, different techniques for approximating the model’s Hessian and the substitution of the trust-region mechanism by a linesearch. The algorithm is also applied to a real case study arising in the context of a recent Belgian transportation model. The performance of the new Monte Carlo algorithm is shown to be competitive with that of existing tools using low discrepancy sequences.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Application of an adaptive Monte Carlo algorithm to mixed logit estimation


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2005-09-09


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Estimation of the mixed logit likelihood function by randomized quasi-Monte Carlo

    Munger, D. / L’Ecuyer, P. / Bastin, F. et al. | Elsevier | 2011



    How good is an estimated logit model? Estimation accuracy analysed by Monte Carlo simulations

    Brundell-Freij / European Transport Forum | British Library Conference Proceedings | 1997


    Logit kernel (or mixed logit) models for large multidimensional choice problems: identification and estimation

    Bowman, J. L. / Association for European Transport | British Library Conference Proceedings | 2005