Fitting Mixed Logit Models by Using Maximum Simulated Likelihood

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood

In this paper we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold: First, we provide a description of the technical implementation of the estimation routine and discuss its properties. Further, we compare our estimation routine to the Stata program gllamm which so...

متن کامل

A Comparison of Hierarchical Bayes and Maximum Simulated Likelihood for Mixed Logit

Mixed logit is a flexible discrete choice model that allows for random coefficients and/or error components that induce correlation over alternatives and time. Procedures for estimating mixed logits have been developed within both the classical (e.g., Revelt and Train, 1998) and Bayesian traditions (Sawtooth Software, 1999.) Asymptotically, the two procedures provide the same information, and H...

متن کامل

Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model

This paper proposes the use of a quasi-random sequence for the estimation of the mixed multinomial logit model. The mixed multinomial structure is a flexible discrete choice formulation which accommodates general patterns of competitiveness as well as heterogeneity across individuals in sensitivity to exogenous variables. The estimation of this model has been achieved in the past using the pseu...

متن کامل

Multivariate probit regression using simulated maximum likelihood

We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regression model and describe and illustrate a Stata program mvprobit for this purpose.

متن کامل

Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated observations, we estimate the unknown density of the model nonparametrically by kernel methods, and then...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: The Stata Journal: Promoting communications on statistics and Stata

سال: 2007

ISSN: 1536-867X,1536-8734

DOI: 10.1177/1536867x0700700306