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The Devil is in the Detail: Hints for Practical Optimisation

Author

Listed:
  • Christensen, T.M.

    (School of Economics and Finance, Queensland University of Technology)

  • Hurn, A.S.

    (School of Economics and Finance, Queensland University of Technology)

  • Lindsay, K.A.

    (Department of Mathematics, 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

  • Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2008. "The Devil is in the Detail: Hints for Practical Optimisation," Economic Analysis and Policy, Elsevier, vol. 38(2), pages 345-368, September.
    • Handle: RePEc:eee:ecanpo:v:38:y:2008:i:2:p:345-368
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    5. 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.
    6. 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.
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    Cited by:

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    2. 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.
    3. Gehrig, Thomas & Haas, Marlene, 2014. "Lehman Brothers: What Did Markets Know?," CEPR Discussion Papers 9893, C.E.P.R. Discussion Papers.
    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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