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The Devil is in the Detail: Hints for Practical Optimisation

Author

Listed:
  • T M Christensen

    (QUT)

  • A S Hurn

    (QUT)

  • K A Lindsay

    (University of Glasgow)

Abstract

Finding the minimum of an objective function, such as a least squares or negative log-likelihood function, with respect to the unknown model parameters is a problem often encountered in econometrics. Consequently, students of econometrics and applied econometricians are usually well-grounded in the broad differences between the numerical procedures employed to solve these problems. Often, however, relatively little time is given to understanding the practical subtleties of implementing these schemes when faced with illbehaved problems. This paper addresses some of the details involved in practical optimisation, such as dealing with constraints on the parameters, specifying starting values, termination criteria and analytical gradients, and illustrates some of the general ideas with several instructive examples.

Suggested Citation

  • T M Christensen & A S Hurn & K A Lindsay, 2008. "The Devil is in the Detail: Hints for Practical Optimisation," NCER Working Paper Series 32, National Centre for Econometric Research.
    • Handle: RePEc:qut:auncer:2008-21
    as

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    File URL: http://www.ncer.edu.au/papers/documents/NCER_WpNo32Aug08.pdf
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. A. S. Hurn & J. I. Jeisman & K. A. Lindsay, 0. "Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 390-455.
    3. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    4. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    5. Chan, K C, et al, 1992. "An Empirical Comparison of Alternative Models of the Short-Term Interest Rate," Journal of Finance, American Finance Association, vol. 47(3), pages 1209-1227, July.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
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    Cited by:

    1. Carolina Effio Saldivar & José Herskovits & Juan Pablo Luna & Claudia Sagastizábal, 2019. "Multidimensional Calibration Of Crude Oil And Refined Products Via Semidefinite Programming Techniques," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-31, February.
    2. Gehrig, Thomas & Haas, Marlene, 2014. "Lehman Brothers: What Did Markets Know?," CEPR Discussion Papers 9893, C.E.P.R. Discussion Papers.
    3. David E. Allen & Michael McAleer, 2018. "Theoretical and Empirical Differences between Diagonal and Full BEKK for Risk Management," Energies, MDPI, vol. 11(7), pages 1-19, June.
    4. Gehrig, Thomas & Haas, Marlene, 2016. "Anomalous Trading Prior to Lehman Brothers' Failure," CEPR Discussion Papers 11194, C.E.P.R. Discussion Papers.

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    More about this item

    Keywords

    gradient algorithms; unconstrained optimisation; generalised method of moments.;
    All these keywords.

    JEL classification:

    • 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

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