Working paper/research report

Publication year: 2008

URL: https://ssrn.com/abstract=1140655

Title of series: Swiss Finance Institute Research Paper Series

Number in series: 08-12

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.

Harvard Citation style: Gilli, M. and Winker, P. (2008) A Review of Heuristic Optimization Methods in Econometrics. (Swiss Finance Institute Research Paper Series, 08-12). Genève: Swiss Finance Institute. https://ssrn.com/abstract=1140655

APA Citation style: Gilli, M., & Winker, P. (2008). A Review of Heuristic Optimization Methods in Econometrics. (Swiss Finance Institute Research Paper Series, 08-12). Swiss Finance Institute. https://ssrn.com/abstract=1140655