Differences Between Classical and Bayesian Estimates for Mixed Logit Models: A Replication Study. (22nd March 2016)

• Record Type: Journal Article
• Authors: Elshiewy, Ossama; Zenetti, German; Boztug, Yasemin
• Is Part Of: Journal of applied econometrics. Volume 32:Number 2(2017)
• Page Start: 470
• Page End: 476
• Publication Date: 2016-03-22
• Subjects: Econometrics -- Periodicals; 330.015195
• DOI: 10.1002/jae.2513
• Languages: English
• ISSNs: 0883-7252

Abstract

Summary: The mixed logit model is widely used in applied econometrics. Researchers typically rely on the free choice between the classical and Bayesian estimation approach. However, empirical evidence of the similarity of their parameter estimates is sparse. The presumed similarity is mainly based on one empirical study that analyzes a single dataset (Huber J, Train KE. 2001. On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters 12 (3): 259–269). Our replication study offers a generalization of their results by comparing classical and Bayesian parameter estimates from six additional datasets and specifically for panel versus cross‐sectional data. In general, our results suggest that the two methods provide similar results, with less similarity for cross‐sectional data than for panel data. Copyright © 2016 John Wiley & Sons, Ltd.