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EM algorithms for ordered probit models with endogenous regressors

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  • Hiroyuki Kawakatsu
  • Ann G. Largey
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    Abstract

    We propose an EM algorithm to estimate ordered probit models with endogenous regressors. The proposed algorithm has a number of computational advantages in comparison to direct numerical maximization of the (limited information) log-likelihood function. First, the sequence of conditional M(aximization)-steps can all be computed analytically. Second, the algorithm updates the model parameters so that positive definiteness of the covariance matrix and monotonicity of cutpoints are naturally satisfied. Third, the variance parameters normalized for identification can be activated to accelerate convergence of the algorithm. The algorithm can be applied to models with dummy endogenous, continuous endogenous or latent endogenous regressors. A small Monte Carlo simulation experiment examines the finite sample performance of the proposed algorithms. Copyright The Author(s). Journal compilation Royal Economic Society 2009

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    Bibliographic Info

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

    Volume (Year): 12 (2009)
    Issue (Month): 1 (03)
    Pages: 164-186

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    Handle: RePEc:ect:emjrnl:v:12:y:2009:i:1:p:164-186

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    Cited by:
    1. Giulia BETTIN & Riccardo LUCCHETTI, 2010. "Interval Regression Models with;Endogenous Explanatory Variables," Working Papers 339, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Vargas, Jose P Mauricio, 2012. "Binding Constraints: Does Firm Size Matter?," MPRA Paper 41286, University Library of Munich, Germany.
    3. Giulia Bettin & Riccardo Lucchetti, 2009. "Instrumental Variable Interval Regression," EHUCHAPS, Universidad del País Vasco - Facultad de Ciencias Económicas y Empresariales.

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