Genetic Algorithms: A Tool for Optimization in Econometrics - Basic Concept and an Example for Empirical Applications
Centre for European Economic Research (ZEW); Catholic University of Leuven (KUL)
Centre for European Economic Research (ZEW)
July 15, 2002
ZEW Discussion Paper No. 02-41
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, C63working papers series
Date posted: October 12, 2002
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