Genetic Algorithms: A Tool for Optimization in Econometrics - Basic Concept and an Example for Empirical Applications

24 Pages Posted: 12 Oct 2002

See all articles by Dirk Czarnitzki

Dirk Czarnitzki

Centre for European Economic Research (ZEW); Catholic University of Leuven (KUL)

Thorsten Doherr

ZEW – Leibniz Centre for European Economic Research

Date Written: July 15, 2002

Abstract

This paper discusses a tool for optimization of econometric models based on genetic algorithms. First, we briefly describe the concept of this optimization technique. Then, we explain the design of a specifically developed algorithm and apply it to a difficult econometric problem, the semiparametric estimation of a censored regression model. We carry out some Monte Carlo simulations and compare the genetic algorithm with another technique, the iterative linear programming algorithm, to run the censored least absolute deviation estimator. It turns out that both algorithms lead to similar results in this case, but that the proposed method is computationally more stable than its competitor.

Keywords: Genetic Algorithm, Semiparametrics, Monte Carlo Simulation

JEL Classification: C14, C25, C45, C61, C63

Suggested Citation

Czarnitzki, Dirk and Doherr, Thorsten, Genetic Algorithms: A Tool for Optimization in Econometrics - Basic Concept and an Example for Empirical Applications (July 15, 2002). ZEW Discussion Paper No. 02-41, Available at SSRN: https://ssrn.com/abstract=320987 or http://dx.doi.org/10.2139/ssrn.320987

Dirk Czarnitzki (Contact Author)

Centre for European Economic Research (ZEW) ( email )

P.O. Box 10 34 43
Mannheim, 68034
Germany

Catholic University of Leuven (KUL) ( email )

Faculty of Economics and Business
Naamsestraat 69
Leuven, 3000
Belgium
+32 16 326906 (Phone)
+32 16 325799 (Fax)

HOME PAGE: http://www.econ.kuleuven.be/msi/faculty_members.htm

Thorsten Doherr

ZEW – Leibniz Centre for European Economic Research ( email )

P.O. Box 10 34 43
L 7,1
D-68161 Mannheim
Germany

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