arXiv 10 Oct 2019 · Econometrics
arXiv:1910.04883 · PDF · DOI · OpenAlex · Extracted main text
Beliefs are important determinants of an individual's choices and economic outcomes, so understanding how they comove and differ across individuals is of considerable interest. Researchers often rely on surveys that report individual beliefs as qualitative data. We propose using a Bayesian hierarchical latent class model to analyze the comovements and observed heterogeneity in categorical survey responses. 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 responses 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Card (1995) Using geographic variation in college proximity to estimate the return to schooling | 0.874 | 7 | 2 | 100% |
| 2 | Erosheva (2002) Grade of Membership and Latent Structures with Application to Disability Survey Data | 0.737 | 3 | 2 | 100% |
| 3 | Manski (2004) Measuring Expectations | 0.737 | 3 | 2 | 100% |
| 4 | Blei, Ng, and Jordan (2003) Latent Dirichlet Allocation | 0.644 | 2 | 2 | 100% |
| 5 | Blei and Lafferty (2006) Dynamic Topic Models | 0.644 | 2 | 2 | 100% |
| 6 | Ruiz, Athey, Blei, et al (2020) Shopper: A probabilistic model of consumer choice with substitutes and complements | 0.644 | 2 | 2 | 100% |
| 7 | Welling and Teh (2011) Bayesian Learning via Stochastic Gradient Langevin Dynamics | 0.585 | 3 | 1 | 100% |
| 8 | Fox and Jordan (2013) Mixed membership models for time series | 0.511 | 2 | 1 | 100% |
| 9 | Wang and Blei (2018) The blessings of multiple causes | 0.511 | 2 | 1 | 100% |
| 10 | Bhadury, Chen, Zhu, and Liu (2016) Scaling Up Dynamic Topic Models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 67 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data | 0.965 | 10 | 4 |
| 2 | Inference for Regression with Variables Generated by AI or Machine Learning | 0.644 | 2 | 2 |