Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/24644
Authors: 
Doherr, Thorsten
Czarnitzki, Dirk
Year of Publication: 
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

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