Review of Heuristic Optimization Methods in Econometrics
AbstractEstimation 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.
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Bibliographic InfoPaper provided by COMISEF in its series Working Papers with number 001.
Length: 48 pages
Date of creation: 09 Sep 2008
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Other versions of this item:
- Manfred GILLI & Peter WINKER, . "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
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