Gradient methods in FIML estimation of econometric models
Through Monte Carlo experiments, this paper compares the performances of different gradient optimization algorithms, when performing full information maximum likelihood (FIML) estimation of econometric models. Different matrices are used (Hessian, outer products matrix, GLS-type matrix, as well as a mixture of them).
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- Calzolari, Giorgio & Panattoni, Lorenzo, 1983. "Hessian and approximated Hessian matrices in maximum likelihood estimation: a Monte Carlo study," MPRA Paper 28847, University Library of Munich, Germany.
- Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665 National Bureau of Economic Research, Inc.
- Parke, William R, 1982. "An Algorithm for FIML and 3SLS Estimation of Large Nonlinear Models," Econometrica, Econometric Society, vol. 50(1), pages 81-95, January.
- Klein, Lawrence R, 1969. "Estimation on Interdependent Systems in Macroeconometrics," Econometrica, Econometric Society, vol. 37(2), pages 171-92, April.
- Dagenais, Marcel G, 1978. "The Computation of FIML Estimates as Iterative Generalized Least Squares Estimates in Linear and Nonlinear Simultaneous Equations Models," Econometrica, Econometric Society, vol. 46(6), pages 1351-62, November.
- Belsley, David A., 1980. "On the efficient computation of the nonlinear full-information maximum-likelihood estimator," Journal of Econometrics, Elsevier, vol. 14(2), pages 203-225, October.
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