Hessian and approximated Hessian matrices in maximum likelihood estimation: a Monte Carlo study
AbstractFull information maximum likelihood estimation of econometric models, linear and nonlinear in variables, is performed by means of two gradient algorithms, using either the Hessian matrix or a computationally simpler approximation. In the first part of the paper, the behavior of the two methods in getting the optimum is investigated with Monte Carlo experimentation on some models of small and medium size. In the second part of the paper, the behavior of the two matrices in producing estimates of the asymptotic covariance matrix of coefficients is analyzed and, again. experimented with Monte Carlo on the same models. Some systematic differences are evidenced.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 28847.
Date of creation: Aug 1983
Date of revision:
Hessian matrix; full information maximum likelihood; Newton like methods; gradient methods; covariance matrix estimators;
Find related papers by JEL classification:
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
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