Arbeitspapier
Using a Laplace approximation to estimate the Random coefficients logit model by non-linear least squares
Current methods of estimating the random coefficients logit model employ simulations of the distribution of the taste parameters through pseudo-random sequences. These methods suffer from difficulties in estimating correlations between parameters and computational limitations such as the curse of dimensionality. This paper provides a solution to these problems by approximating the integral expression of the expected choice probability using a multivariate extension of the Laplace approximation. Simulation results reveal that our method performs very well, both in terms of accuracy and computational time.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP20/06
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Harding, Matthew C.
Hausman, Jerry
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2006
- DOI
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doi:10.1920/wp.cem.2006.2006
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Harding, Matthew C.
- Hausman, Jerry
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2006
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Partitioned least squares
Least-squares collocation
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The Constrained Least-Squares Paradox
Least-squares variance component estimation