View in EDS

Estimation and welfare analysis from mixed logit models with large choice sets.

Saved in:
Bibliographic Details
Title: Estimation and welfare analysis from mixed logit models with large choice sets.
Authors: von Haefen, Roger H.1 roger_von_haefen@ncsu.edu, Domanski, Adam2 domanski@econw.com
Source: Journal of Environmental Economics & Management. Jul2018, Vol. 90, p101-118. 18p.
Subjects: Welfare economics, Logits, Statistical sampling, Expectation-maximization algorithms, Parameter estimation, Discrete choice models
Abstract: We show how McFadden's sampling of alternatives approach and the expectation-maximization (EM) algorithm can be used to consistently estimate latent-class, mixed logit models in applications with large choice sets. We present Monte Carlo evidence confirming our approach works well in small samples, apply the method to a dataset of Wisconsin angler site destination choices, and report welfare estimates for several policy scenarios. Of interest to applied researchers, our results quantify the tradeoffs between model run-time, accuracy, and precision of welfare estimates associated with samples of different sizes. Moreover, although our results confirm that larger efficiency losses arise with smaller samples as theory would predict, they also suggest that depending on researcher needs, random samples as small as 28 alternatives (5% of the full set of alternatives in our application) can produce relatively accurate welfare estimates that are useful for exploratory modeling, sensitivity analysis, and policy purposes. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Environmental Economics & Management is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:We show how McFadden's sampling of alternatives approach and the expectation-maximization (EM) algorithm can be used to consistently estimate latent-class, mixed logit models in applications with large choice sets. We present Monte Carlo evidence confirming our approach works well in small samples, apply the method to a dataset of Wisconsin angler site destination choices, and report welfare estimates for several policy scenarios. Of interest to applied researchers, our results quantify the tradeoffs between model run-time, accuracy, and precision of welfare estimates associated with samples of different sizes. Moreover, although our results confirm that larger efficiency losses arise with smaller samples as theory would predict, they also suggest that depending on researcher needs, random samples as small as 28 alternatives (5% of the full set of alternatives in our application) can produce relatively accurate welfare estimates that are useful for exploratory modeling, sensitivity analysis, and policy purposes. [ABSTRACT FROM AUTHOR]
ISSN:00950696
DOI:10.1016/j.jeem.2018.05.002