Selection of Minimal Variable Subsets with the help of Genetic Algorithms in Economic Analysis

Esmail Bonakdarian, Hans J. Czap and Natalia V. Czap
Additional contact information
Esmail Bonakdarian: Franklin University, Division of Computer and Information Sciences, 201 S. Grant Avenue, Columbus, OH 43215, USA
Hans J. Czap: University of Michigan – Dearborn, Department of Social Sciences, 4901 Evergreen Road, Dearborn, MI 48128, USA. Phone: ++1-402-570-3837; E-mail: hczap@umd.umich.edu
Natalia V. Czap: University of Michigan – Dearborn, Department of Social Sciences, 4901 Evergreen Road, Dearborn, MI 48128, USA

Journal of Interdisciplinary Economics, 2009, vol. 21, issue 4, 407-425

Abstract: In this paper we demonstrate the use of Genetic Algorithms in the selection of significant variables from a large set of available variables used to explain the observed behavior of subjects in an economic experiment. Standard regression analysis requires assumptions on a functional form and may thus prevent us from finding all relevant relationships. When using a more flexible functional form the number of coefficients (corresponding to different variables and their interactions) grows exponentially. Hence, our goal in this paper is to select the smallest set of variables with the largest “explanatory†value. We use a variation of a non-traditional type of Genetic Algorithm, CHC, to “evolve†this preferred minimal set of relevant variables. We compare this approach with estimation based on basic linear (least squares) regression models and regression models chosen by using the stepwise regression method. Additionally, we also evaluate the effectiveness of various fitness criteria in our genetic algorithm’s fitness function. We believe that an evolutionary computation approach provides a useful alternative and supplementary method to more traditional methods by offering potentially new useful subsets of significant variables warranting further exploration and a limit to the number of coefficients in outputs.

Keywords: step-wise regression; genetic algorithm; variable reduction methods; global optimum (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://jie.sagepub.com/content/21/4/407.abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jinter:v:21:y:2009:i:4:p:407-425

Access Statistics for this article

More articles in Journal of Interdisciplinary Economics
Bibliographic data for series maintained by SAGE Publications ().