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Computationally feasible estimation of the covariance structure in Generalized linear mixed models(GLMM)

Author

Listed:
  • Carling, Kenneth

    () (Department of Business, Economics, Statistics and Informatics)

  • Alam, Moudud

    () (Department of Business, Economics, Statistics and Informatics)

Abstract

In this paper we discuss how a regression model, with a non-continuous response variable, that allows for dependency between observations should be estimated when observations are clustered and there are repeated measurements on the subjects. The cluster sizes are assumed to be large. We …nd that the conventional estimation technique suggested by the literature on Generalized Linear Mixed Models (GLMM) is slow and often fails due to non-convergence and lack of memory on standard PCs. We suggest to estimate the random e¤ects as …xed e¤ects by GLM and derive the covariance matrix from these estimates. A simulation study shows that our proposal is feasible in terms of Mean-Square Error and computation time. We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal depending on the size of the clusters.

Suggested Citation

  • Carling, Kenneth & Alam, Moudud, 2007. "Computationally feasible estimation of the covariance structure in Generalized linear mixed models(GLMM)," Working Papers 2007:14, Örebro University, School of Business.
  • Handle: RePEc:hhs:oruesi:2007_014
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    References listed on IDEAS

    as
    1. William Greene, 2004. "The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 98-119, June.
    2. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
    3. James J. Heckman & Robert J. Willis, 1976. "Estimation of a Stochastic Model of Reproduction: An Econometric Approach," NBER Chapters,in: Household Production and Consumption, pages 99-146 National Bureau of Economic Research, Inc.
    4. Yun, Sungcheol & Lee, Youngjo, 2004. "Comparison of hierarchical and marginal likelihood estimators for binary outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 639-650, April.
    5. G. S. Maddala, 1987. "Limited Dependent Variable Models Using Panel Data," Journal of Human Resources, University of Wisconsin Press, vol. 22(3), pages 307-338.
    6. Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series 168, Sveriges Riksbank (Central Bank of Sweden).
    7. Quintana, Fernando A. & Liu, Jun S. & Pino, Guido E. del, 1999. "Monte Carlo EM with importance reweighting and its applications in random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 429-444, February.
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    More about this item

    Keywords

    Monte-Carlo simulations; large sample; interdependence; cluster error;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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