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Genetic Algorithms: A Tool for Optimization in Econometrics - Basic Concept and an Example for Empirical ApplicationsDirk CzarnitzkiCentre for European Economic Research (ZEW); Catholic University of Leuven (KUL) Thorsten DoherrCentre for European Economic Research (ZEW) July 15, 2002 ZEW Discussion Paper No. 02-41 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.
Number of Pages in PDF File: 24 Keywords: Genetic Algorithm, Semiparametrics, Monte Carlo Simulation JEL Classification: C14, C25, C45, C61, C63 working papers seriesDate posted: October 12, 2002Suggested CitationContact Information
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