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USING A LAPLACE APPROXIMATION TO ESTIMATE THE RANDOM COEFFICIENTS LOGIT MODEL BY NONLINEAR LEAST SQUARES

机译:使用Laplace逼近通过非线性最小二乘估计随机系数Logit模型

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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. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.
机译:当前的估计随机系数对数模型的方法采用通过伪随机序列模拟味觉参数分布的方法。这些方法在估计参数和计算限制(例如维数的诅咒)之间的相关性时遇到困难。本文通过使用Laplace近似的多元扩展来近似预期选择概率的积分表达式,为这些问题提供了解决方案。仿真结果表明,我们的方法在准确性和计算时间方面都表现出色。宾夕法尼亚大学经济系和大阪大学社会经济研究所联合会版权所有2007。

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