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The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models

Hole, A.R.; Yoo, H.I.

The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models Thumbnail


Authors

A.R. Hole

H.I. Yoo



Abstract

Applications of random-parameter logit models can be found in various disciplines. These models have non-concave simulated likelihood functions and the choice of starting values is therefore crucial to avoid convergence at an inferior optimum. Little guidance exists, however, on how to obtain good starting values. We apply an estimation strategy which makes joint use of heuristic global search routines and gradient-based algorithms. The central idea is to use heuristic routines to locate a starting point which is likely to be close to the global maximum, and then to use gradient-based algorithms to refine this point further. Using four empirical data sets, as well as simulated data, we find that the strategy proposed locates higher maxima than more conventional estimation strategies.

Citation

Hole, A., & Yoo, H. (2017). The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models. Journal of the Royal Statistical Society: Series C, 66(5), 997-1013. https://doi.org/10.1111/rssc.12209

Journal Article Type Article
Acceptance Date Nov 28, 2016
Online Publication Date Jan 18, 2017
Publication Date Nov 1, 2017
Deposit Date Nov 29, 2016
Publicly Available Date Mar 28, 2024
Journal Journal of the Royal Statistical Society: Series C
Print ISSN 0035-9254
Electronic ISSN 1467-9876
Publisher Royal Statistical Society
Peer Reviewed Peer Reviewed
Volume 66
Issue 5
Pages 997-1013
DOI https://doi.org/10.1111/rssc.12209
Public URL https://durham-repository.worktribe.com/output/1392191

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Accepted Journal Article (647 Kb)
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
© 2017 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.






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