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Simulated maximum likelihood estimation in transition models

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  • THIERRY KAMIONKA
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    Abstract

    In this paper we analyse the problem of modelling individual transitions in the presence of an incomplete sampling scheme. This problem is particularly cumbersome when a continuous time-scale is used for the modelling and when the model incorporates unobserved heterogeneity. This problem arises, for instance, when the observation is made at fixed time points or stops on an interval of time. In order to take this phenomenon into account, we propose to maximize the simulated likelihood using an importance function. The method can be applied to general continuous-time discrete-state-space processes and a broad class of incomplete sampling schemes.

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    Bibliographic Info

    Article provided by Royal Economic Society in its journal The Econometrics Journal.

    Volume (Year): 1 (1998)
    Issue (Month): ConferenceIssue ()
    Pages: C129-C153

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    Handle: RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c129-c153

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    Related research

    Keywords: Simulated maximum likelihood; Incomplete observation scheme; Transition data; Importance sampling density; Monte-Carlo experiments; Discrete choice.;

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    Cited by:
    1. Lechner, Michael & Lollivier, Stefan & Magnac, Thierry, 2005. "Parametric Binary Choice Models," IDEI Working Papers 398, Institut d'Économie Industrielle (IDEI), Toulouse.
    2. Nicholas-James Clavet & Jean-Yves Duclos, 2012. "Le financement des services de garde des enfants: effets sur le travail, le revenu des familles, et les finances publiques," Cahiers de recherche 1216, CIRPEE.
    3. Guy Lacroix & Marie-Ève Brouard, 2011. "Work Absenteeism Due to a Chronic Disease," CIRANO Working Papers 2011s-33, CIRANO.
    4. Denis Fougère & Thierry Kamionka, 2002. "Bayesian Inference for the Mover-Stayer Model in Continuous Time with an Application to Labour Market Transition Data," Working Papers 2002-23, Centre de Recherche en Economie et Statistique.

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