Online Program Home
My Program

Abstract Details

Activity Number: 641 - Recent Advances in Density Mixture Modeling and EM-Like Algorithms: Frequentist and Bayesian Views
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #304260 Presentation
Title: Mixture Methods for Panel Data Models
Author(s): Stephane Bonhomme*
Companies: University of Chicago
Keywords: mixture models; panel data

Mixture models are central to panel data analysis. In this talk I study the ability of parametric and nonparametric mixture methods to identify and estimate nonlinear relationships on longitudinal data. Discrete and continuous mixtures have different strengths and weaknesses. I study their properties in panels of fixed lengths as the size of the cross-section increases, and in an alternative asymptotic where the number of units and time periods tend to infinity jointly. I discuss applications to the estimation of dynamic structural economic models with unobserved heterogeneity, and to the analysis of treatment effects in panel data studies.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program