A review of heuristic optimization methods in econometrics

Manfred Gilli () and Peter Winker

No 08-12, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as evolutionary algorithms (Simulated Annealing, Threshold Accepting), Neural Networks, Genetic Algorithms, Tabu Search, hybrid methods and many others, which have been developed over the last two decades. Although the use of these methods became more standard in several fields of sciences, their use in estimation and modelling in econometrics appears to be still limited. We present an introduction to heuristic optimization methods and provide some examples for which these methods are found to work efficiently.

Keywords: Optimization heuristics; Estimation; Modelling (search for similar items in EconPapers)
JEL-codes: C13 C61 C63 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2008-06
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Citations: View citations in EconPapers (18)

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Related works:
Working Paper: Review of Heuristic Optimization Methods in Econometrics (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp0812

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