Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications

Thorsten Doherr and Dirk Czarnitzki

No 02-41, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

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 (search for similar items in EconPapers)
JEL-codes: C14 C25 C45 C61 C63 (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:677

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