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The behavior of trust-region methods in FIML estimation

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  • Weihs, Claus
  • Calzolari, Giorgio
  • Panattoni, Lorenzo

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.

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File URL: http://mpra.ub.uni-muenchen.de/24122/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 24122.

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Date of creation: 1986
Date of revision: 1987
Publication status: Published in Computing 38.38(1987): pp. 89-100
Handle: RePEc:pra:mprapa:24122

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Related research

Keywords: Econometrics; Monte Carlo methods; numerical methods; trust-region methods; FIML estimation;

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References

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  1. Amemiya, Takeshi, 1977. "The Maximum Likelihood and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model," Econometrica, Econometric Society, Econometric Society, vol. 45(4), pages 955-68, May.
  2. Besley, David A., 1979. "On the computational competitiveness of full-information maximum-likelihood and three-stage least-squares in the estimation of nonlinear, simultaneous-equations models," Journal of Econometrics, Elsevier, Elsevier, vol. 9(3), pages 315-342, February.
  3. Calzolari, Giorgio & Panattoni, Lorenzo & Weihs, Claus, 1987. "Computational efficiency of FIML estimation," Journal of Econometrics, Elsevier, Elsevier, vol. 36(3), pages 299-310, November.
  4. Calzolari, Giorgio & Panattoni, Lorenzo, 1985. "Gradient methods in FIML estimation of econometric models," MPRA Paper 24843, University Library of Munich, Germany.
  5. Belsley, David A., 1980. "On the efficient computation of the nonlinear full-information maximum-likelihood estimator," Journal of Econometrics, Elsevier, Elsevier, vol. 14(2), pages 203-225, October.
  6. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, National Bureau of Economic Research, Inc, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc.
  7. Parke, William R, 1982. "An Algorithm for FIML and 3SLS Estimation of Large Nonlinear Models," Econometrica, Econometric Society, Econometric Society, vol. 50(1), pages 81-95, January.
  8. Calzolari, Giorgio & Panattoni, Lorenzo, 1984. "A Simulation Study on FIML Covariance Matrix," MPRA Paper 28804, University Library of Munich, Germany.
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