Latent Dirichlet Analysis of Categorical Survey Expectations

33 Pages Posted: 18 May 2020 Last revised: 10 Apr 2022

See all articles by Evan Munro

Evan Munro

Stanford University

Serena Ng

Columbia University - Columbia Business School, Economics

Date Written: May 2020

Abstract

Beliefs are important determinants of an individual's choices and economic outcomes, so understanding how they differ across individuals is of considerable interest. Researchers often rely on surveys that report individual expectations as qualitative data. We propose using a Bayesian hierarchical latent class model to summarize and interpret observed heterogeneity in categorical expectations data. We show that the statistical model corresponds to an economic structural model of information acquisition, which guides interpretation and estimation of the model parameters. An algorithm based on stochastic optimization is proposed to estimate a model for repeated surveys when beliefs follow a dynamic structure and conjugate priors are not appropriate. Guidance on selecting the number of belief types is also provided. Two examples are considered. The first shows that there is information in the Michigan survey responses beyond the consumer sentiment index that is officially published. The second shows that belief types constructed from survey responses can be used in a subsequent analysis to estimate heterogeneous returns to education.

Suggested Citation

Munro, Evan and Ng, Serena, Latent Dirichlet Analysis of Categorical Survey Expectations (May 2020). NBER Working Paper No. w27182, Available at SSRN: https://ssrn.com/abstract=3603812

Evan Munro (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

Serena Ng

Columbia University - Columbia Business School, Economics ( email )

420 West 118th Street
New York, NY 10027
United States

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