The behavior of trust-region methods in FIML estimation

Claus Weihs, Giorgio Calzolari and Lorenzo Panattoni

MPRA Paper from University Library of Munich, Germany

Abstract: This 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.

Keywords: Econometrics; Monte Carlo methods; numerical methods; trust-region methods; FIML estimation (search for similar items in EconPapers)
JEL-codes: C30 C61 C87 (search for similar items in EconPapers)
Date: 1986, Revised 1987
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Published in Computing 38.38(1987): pp. 89-100

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