Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares
Matthew Harding () and
No CWP20/06, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
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.
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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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