Using A Laplace Approximation To Estimate The Random Coefficients Logit Model By Nonlinear Least Squares
AbstractCurrent 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 article 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, in terms of both accuracy and computational time. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.
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Bibliographic InfoArticle provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.
Volume (Year): 48 (2007)
Issue (Month): 4 (November)
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Other versions of this item:
- Matthew Harding & Jerry Hausman, 2006. "Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares," CeMMAP working papers CWP20/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Matthew C. Harding & Jerry Hausman, 2006. "Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares," CeMMAP working papers CWP01/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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