On the Variance Covariance Matrix of the Maximum Likelihood Estimator of a Discrete Mixture
AbstractThe estimation of models involving discrete mixtures is a common practice in econometrics, for example to account for unobserved heterogeneity. However, the literature is relatively uninformative about the measurement of the precision of the parameters. This note provides an analytical expression for the observed information matrix in terms of the gradient and hessian of the latent model when the number of components of the discrete mixture is known. This in turn allows for the estimation of the variance covariance matrix of the ML estimator of the parameters. I discuss further two possible applications of the result: the acceleration of the EM algorithm and the specification testing with the information matrix test.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0211001.
Length: 16 pages
Date of creation: 05 Nov 2002
Date of revision:
Note: Type of Document - pdf; prepared on pc; pages: 16
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Discrete Mixtures; EM Algorithm; Variance Covariance Matrix; Observed Information;
Other versions of this item:
- Gauthier Lanot, 2002. "On the Variance Covariance Matrix of the Maximum Likelihood Estimator of a Discrete Mixture," Keele Economics Research Papers KERP 2002/07, Centre for Economic Research, Keele University.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-11-10 (All new papers)
- NEP-ECM-2002-11-13 (Econometrics)
- NEP-RMG-2002-11-10 (Risk Management)
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