Using a laplace approximation to estimate the random coefficients logit model by nonlinear least squares
成果类型：
Article
署名作者：
Harding, Matthew C.; Hausman, Jerry
署名单位：
Massachusetts Institute of Technology (MIT); Stanford University
刊物名称：
INTERNATIONAL ECONOMIC REVIEW
ISSN/ISSBN：
0020-6598
DOI：
10.1111/j.1468-2354.2007.00463.x
发表日期：
2007
页码：
1311-1328
关键词：
摘要：
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 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.