IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/4634.html
   My bibliography  Save this paper

Performance of Differential Evolution Method in Least Squares Fitting of Some Typical Nonlinear Curves

Author

Abstract

No foolproof method exists to fit nonlinear curves to data or estimate the parameters of an intrinsically nonlinear function. Some methods succeed at solving a set of problems but fail at the others. The Differential Evolution (DE) method of global optimization is an upcoming method that has shown its power to solve difficult nonlinear optimization problems. In this study we use the DE to solve some nonlinear least squares problems given by the National Institute of Standards and Technology (NIST), US Department of Commerce, USA and some other challenge problems posed by the CPC-X Software (the makers of the AUTO2FIT software). The DE solves the test problems given by the NIST and most of the challenge problems posed by the CPC-X, doing marginally better than the AUTO2FIT software in a few cases.

Suggested Citation

  • Mishra, SK, 2007. "Performance of Differential Evolution Method in Least Squares Fitting of Some Typical Nonlinear Curves," MPRA Paper 4634, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:4634
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/4634/1/MPRA_paper_4634.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/4656/1/MPRA_paper_4656.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. George E. P. Box, 1957. "Evolutionary Operation: A Method for Increasing Industrial Productivity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 6(2), pages 81-101, June.
    2. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    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. Mishra, SK, 2008. "Construction of composite indices in presence of outliers," MPRA Paper 8874, University Library of Munich, Germany.
    2. Sudhanshu K. MISHRA, 2016. "Shapley Value Regression and the Resolution of Multicollinearity," Journal of Economics Bibliography, KSP Journals, vol. 3(3), pages 498-515, September.
    3. Sudhanshu Mishra, 2007. "Least squares estimation of joint production functions by the differential evolution method of global optimization," Economics Bulletin, AccessEcon, vol. 3(51), pages 1-13.
    4. repec:ebl:ecbull:v:3:y:2007:i:51:p:1-13 is not listed on IDEAS
    5. S K Mishra, 2010. "A Brief History of Production Functions," The IUP Journal of Managerial Economics, IUP Publications, vol. 0(4), pages 6-34, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Baudin & Robert Stelter, 2022. "The rural exodus and the rise of Europe," Journal of Economic Growth, Springer, vol. 27(3), pages 365-414, September.
    2. Luca Benati & Paolo Surico, 2009. "VAR Analysis and the Great Moderation," American Economic Review, American Economic Association, vol. 99(4), pages 1636-1652, September.
    3. Asgharian, Hossein & Hess, Wolfgang & Liu, Lu, 2013. "A spatial analysis of international stock market linkages," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4738-4754.
    4. Luca Benati & Paolo Surico, 2008. "Evolving U.S. Monetary Policy and The Decline of Inflation Predictability," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 634-646, 04-05.
    5. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    6. Jason Matthew DeBacker, 2015. "Flip‐Flopping: Ideological Adjustment Costs In The United States Senate," Economic Inquiry, Western Economic Association International, vol. 53(1), pages 108-128, January.
    7. Luca Benati & Pierpaolo Benigno, 2023. "Gibson s Paradox and the Natural Rate of Interest," Diskussionsschriften dp2303, Universitaet Bern, Departement Volkswirtschaft.
    8. Haan, Peter & Prowse, Victoria L., 2010. "The Design of Unemployment Transfers: Evidence from a Dynamic Structural Life-Cycle Model," IZA Discussion Papers 4792, Institute of Labor Economics (IZA).
    9. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    10. Green, Rikard & Larsson, Karl & Lunina, Veronika & Nilsson, Birger, 2018. "Cross-commodity news transmission and volatility spillovers in the German energy markets," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 231-243.
    11. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    12. Roman Sustek, 2011. "Monetary Business Cycle Accounting," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(4), pages 592-612, October.
    13. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition, and stratification," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 117-139, August.
    14. Martin Andreasen, 2010. "How to Maximize the Likelihood Function for a DSGE Model," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 127-154, February.
    15. Max Jerrell, 2000. "Applications Of Public Global Optimization Software To Difficult Econometric Functions," Computing in Economics and Finance 2000 161, Society for Computational Economics.
    16. Robert G. King & Alexander Wolman & Michael Dotsey, 2009. "Inflation and Real Activity with Firm Level Productivity Shocks," 2009 Meeting Papers 367, Society for Economic Dynamics.
    17. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    18. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    19. Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
    20. Pudney, Stephen, 2011. "Perception and retrospection: The dynamic consistency of responses to survey questions on wellbeing," Journal of Public Economics, Elsevier, vol. 95(3), pages 300-310.

    More about this item

    Keywords

    Nonlinear least squares; curve fitting; Differential Evolution; global optimization; AUTO2FIT; CPC-X Software; NIST; National Institute of Standards and Technology; test problems;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:4634. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.