Fourth order pseudo maximum likelihood methods
AbstractWe extend PML theory to account for information on the conditional moments up to order four, but without assuming a parametric model, to avoid a risk of misspecification of the conditional distribution. The key statistical tool is the quartic exponential family, which allows us to generalize the PML2 and QGPML1 methods proposed in Gourieroux, Monfort, and Trognon (1984) to PML4 and QGPML2 methods, respectively. An asymptotic theory is developed. The key numerical tool that we use is the Gauss-Freud integration scheme that solves a computational problem that has previously been raised in several fields. Simulation exercises demonstrate the feasibility and robustness of the methods.
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Bibliographic InfoPaper provided by HAL in its series Post-Print with number peer-00815562.
Date of creation: 19 Apr 2011
Date of revision:
Publication status: Published, Journal of Econometrics, 2011
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C01; C13; C16; C22; Quartic exponential family; Pseudo maximum likelihood; Skewness; Kurtosis;
Other versions of this item:
- Alberto Holly & Alain Montfort & Michael Rockinger, 2008. "Fourth order pseudo maximum likelihood methods," Working Papers 0802, University of Lausanne, Institute of Health Economics and Management (IEMS).
- Alberto HOLLY & Alain MONFORT & Michael ROCKINGER, 2011. "Fourth Order Pseudo Maximum Likelihood Methods," Working Papers 2011-05, Centre de Recherche en Economie et Statistique.
- Alberto HOLLY & Alain MONFORT & Michael ROCKINGER, . "Fourth Order Pseudo Maximum Likelihood Methods," Swiss Finance Institute Research Paper Series 09-23, Swiss Finance Institute.
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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