The behavior of trust-region methods in FIML estimation
AbstractThis paper presents a Monte-Carlo study on the practical reliability of numerical algorithms for FIML-estimation in nonlinear econometric models. The performance of different techniques of Hessian approximation in trust-region algorithms is compared regarding their "robustness" against "bad" starting points and their "global" and "local" convergence speed, i.e. the gain in the objective function, caused by individual iteration steps far off from and near to the optimum. Concerning robustness and global convergence speed the crude GLS-type Hessian approximations performed best, efficiently exploiting the special structure of the likelihood function. But, concerning local speed, general purpose techniques were strongly superior. So, some appropriate mixtures of these two types of approximations turned out to be the only techniques to be recommended.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 24122.
Date of creation: 1986
Date of revision: 1987
Publication status: Published in Computing 38.38(1987): pp. 89-100
Econometrics; Monte Carlo methods; numerical methods; trust-region methods; FIML estimation;
Find related papers by JEL classification:
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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