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

Author

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  • Doherr, Thorsten
  • Czarnitzki, Dirk

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.

Suggested Citation

  • Doherr, Thorsten & Czarnitzki, Dirk, 2002. "Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications," ZEW Discussion Papers 02-41, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:677
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    References listed on IDEAS

    as
    1. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    2. McManus, Walter S, 1985. "Estimates of the Deterrent Effect of Capital Punishment: The Importance of the Researcher's Prior Beliefs," Journal of Political Economy, University of Chicago Press, vol. 93(2), pages 417-425, April.
    3. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
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    Cited by:

    1. Adeola Oyenubi, 2019. "Diversification Measures and the Optimal Number of Stocks in a Portfolio: An Information Theoretic Explanation," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1443-1471, December.
    2. Zhou, Xiuqing & Wang, Jinde, 2005. "A genetic method of LAD estimation for models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 451-466, March.
    3. Makram El-Shagi, 2011. "An evolutionary algorithm for the estimation of threshold vector error correction models," International Economics and Economic Policy, Springer, vol. 8(4), pages 341-362, December.

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    More about this item

    Keywords

    Genetic Algorithm; Semiparametrics; Monte Carlo Simulation;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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