EconStor >
Zentrum für Europäische Wirtschaftsforschung (ZEW), Mannheim >
ZEW Discussion Papers >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/24644
  
Title:Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications PDF Logo
Authors:Doherr, Thorsten
Czarnitzki, Dirk
Issue Date:2002
Series/Report no.:ZEW Discussion Papers 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.
Subjects:Genetic Algorithm
Semiparametrics
Monte Carlo Simulation
JEL:C25
C45
C61
C63
C14
Document Type:Working Paper
Appears in Collections:Publikationen von Forscherinnen und Forschern des ZEW
ZEW Discussion Papers

Files in This Item:
File Description SizeFormat
dp0241.pdf281.02 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/24644

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.