IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v9y1996i4p275-98.html
   My bibliography  Save this article

Functional Search in Economics Using Genetic Programming

Author

Listed:
  • Schmertmann, Carl P

Abstract

This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists. Citation Copyright 1996 by Kluwer Academic Publishers.

Suggested Citation

  • Schmertmann, Carl P, 1996. "Functional Search in Economics Using Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 9(4), pages 275-298, November.
  • Handle: RePEc:kap:compec:v:9:y:1996:i:4:p:275-98
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Evans, Martin D D & Lewis, Karen K, 1995. " Do Expected Shifts in Inflation Affect Estimates of the Long-Run Fisher Relation?," Journal of Finance, American Finance Association, vol. 50(1), pages 225-253, March.
    2. Franses, Philip Hans & Paap, Richard, 1999. "Does Seasonality Influence the Dating of Business Cycle Turning Points?," Journal of Macroeconomics, Elsevier, vol. 21(1), pages 79-92, January.
    3. Franses, Philip Hans, 1996. "Periodicity and Stochastic Trends in Economic Time Series," OUP Catalogue, Oxford University Press, number 9780198774549.
    4. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
    5. Granger, Clive W J, 1993. "Strategies for Modelling Nonlinear Time-Series Relationships," The Economic Record, The Economic Society of Australia, vol. 69(206), pages 233-238, September.
    6. Arteche, Josu & Robinson, Peter M., 1998. "Seasonal and cyclical long memory," LSE Research Online Documents on Economics 2241, London School of Economics and Political Science, LSE Library.
    7. Dick van Dijk 1 & Birgit Strikholm & Timo Teräsvirta, 2003. "The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 79-98, June.
    8. Warne Anders & Vredin Anders, 2006. "Unemployment and Inflation Regimes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, pages 1-52.
    9. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, pages 59-75.
    10. Garcia, Rene & Perron, Pierre, 1996. "An Analysis of the Real Interest Rate under Regime Shifts," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 111-125, February.
    11. Mohamed Safouane Ben Aissa & Mohamed Boutahar & Jamel Jouini, 2004. "Bai and Perron's and spectral density methods for structural change detection in the US inflation process," Applied Economics Letters, Taylor & Francis Journals, vol. 11(2), pages 109-115.
    12. Miron, Jeffrey A & Beaulieu, J Joseph, 1996. "What Have Macroeconomists Learned about Business Cycles form the Study of Seasonal Cycles?," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 54-66, February.
    13. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521565882, December.
    14. Canova, Fabio & Ghysels, Eric, 1994. "Changes in seasonal patterns : Are they cyclical?," Journal of Economic Dynamics and Control, Elsevier, vol. 18(6), pages 1143-1171, November.
    15. Franses, Ph.H.B.F. & de Bruin, P. & van Dijk, D.J.C., 2000. "Seasonal smooth transition autoregression," Econometric Institute Research Papers EI 2000-06/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    17. Franses, Philip Hans & Ooms, Marius, 1997. "A periodic long-memory model for quarterly UK inflation," International Journal of Forecasting, Elsevier, vol. 13(1), pages 117-126, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Duffy, John & McNelis, Paul D., 2001. "Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1273-1303, September.
    2. Uwe Cantner & Bernd Ebersberger & Horst Hanusch & Jens J. Krüger & Andreas Pyka, 2004. "The Twin Peaks in National Income. Parametric and Nonparametric Estimates," Revue économique, Presses de Sciences-Po, vol. 55(6), pages 1127-1144.
    3. Peter Woehrmann & Willi Semmler & Martin Lettau, "undated". "Nonparametric Estimation of the Time-varying Sharpe Ratio in Dynamic Asset Pricing Models," IEW - Working Papers 225, Institute for Empirical Research in Economics - University of Zurich.
    4. Bernd Ebersberger & Uwe Cantner & Horst Hanusch, 2000. "Analyzing Inefficiency Using a Frontier Search Approach," Discussion Paper Series 199, Universitaet Augsburg, Institute for Economics.
    5. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:9:y:1996:i:4:p:275-98. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.