A Toolkit for Optimizing Functions in Economics
Optimization algorithms must be among the most common numerical methods used by economists. Yet, there is surprisingly little guidance on choosing the appropriate one. This problem is most notable with regard to conventional versus global optimizers. Typically, a global optimizer is used when a conventional one fails after substantial ``fiddling'' with a conventional optimizer. This paper introduces three different, easy-to-use, tools (cross-sections, radius plots, and a measure of the non-quadratic behavior of a function) that are designed to indicate when a global optimizer is needed. With their use, researchers should spend less time fiddling and more time generating results.
(This abstract was borrowed from another version of this item.)
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- Veall, Michael R, 1990. "Testing for a Global Maximum in an Econometric Context," Econometrica, Econometric Society, vol. 58(6), pages 1459-1465, November.
- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
- Goffe William L., 1996. "SIMANN: A Global Optimization Algorithm using Simulated Annealing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(3), pages 1-9, October.
- Hoffman, Dennis L. & Schmidt, Peter, 1981. "Testing the restrictions implied by the rational expectations hypothesis," Journal of Econometrics, Elsevier, vol. 15(2), pages 265-287, February.
- Dorsey, Robert E & Mayer, Walter J, 1995.
"Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(1), pages 53-66, January.
- Michael B. Gordy, "undated". "GA.M: A Matlab routine for function maximization using a Genetic Algorithm," Matlab codes ga, , revised 12 Feb 1996.
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