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The Devil is in the Detail: Hints for Practical Optimisation

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Author Info
T M Christensen () (QUT)
A S Hurn () (QUT)
K A Lindsay () (University of Glasgow)

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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.

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File URL: http://www.ncer.edu.au/papers/documents/NCER_WpNo32Aug08.pdf
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Publisher Info
Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 32.

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Length: 26 pages
Date of creation: 18 Aug 2008
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Handle: RePEc:qut:auncer:2008-21

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Web page: http://www.ncer.edu.au
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Related research
Keywords: gradient algorithms unconstrained optimisation generalised method of moments.

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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This page was last updated on 2008-10-23.


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