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

Czarnitzki, Dirk ; Doherr, Thorsten

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URN: urn:nbn:de:bsz:180-madoc-4048
Document Type: Working paper
Year of publication: 2002
The title of a journal, publication series: None
Publication language: English
Institution: Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
MADOC publication series: Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
Subject: 330 Economics
Classification: JEL: C25 C45 C63 C61 C14 ,
Subject headings (SWD): Optimierung , Generierung , Algorithmus , Monte-Carlo-Simulation
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.
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