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.
This paper is a revised version of CWP01/06.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Jerry Hausman
MIT
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Matthew C. Harding
Stanford University
Working Paper details
- DOI
- 10.1920/wp.cem.2006.2006
- Publisher
- IFS
Suggested citation
Harding, M and Hausman, J. (2006). Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares. London: IFS. Available at: https://ifs.org.uk/publications/using-laplace-approximation-estimate-random-coefficients-logit-model-non-linear-least (accessed: 30 June 2024).
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