Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares

Matthew Harding () and Jerry Hausman

No CWP20/06, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: 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.

Pages: 30 pp.
Date: 2006-10-05
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Related works:
Journal Article: USING A LAPLACE APPROXIMATION TO ESTIMATE THE RANDOM COEFFICIENTS LOGIT MODEL BY NONLINEAR LEAST SQUARES (2007)
Working Paper: Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares (2006)
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