Szymon Sacher, Laura Battaglia, Stephen Hansen
arXiv 16 Jul 2021 · Econometrics · 1 citations (OpenAlex)
arXiv:2107.08112 · PDF · DOI · OpenAlex · Extracted main text
Latent variable models are increasingly used in economics for high-dimensional categorical data like text and surveys. We demonstrate the effectiveness of Hamiltonian Monte Carlo (HMC) with parallelized automatic differentiation for analyzing such data in a computationally efficient and methodologically sound manner. Our new model, Supervised Topic Model with Covariates, shows that carefully modeling this type of data can have significant implications on conclusions compared to a simpler, frequently used, yet methodologically problematic, two-step approach. A simulation study and revisiting Bandiera et al. (2020)'s study of executive time use demonstrate these results. The approach accommodates thousands of parameters and doesn't require custom algorithms specific to each model, making it accessible for applied researchers
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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 | Bandiera, O., Prat, A., Hansen, S., and Sadun, R (2020) CEO Behavior and Firm Performance self | 1.000 | 8 | 4 | 100% |
| 2 | Munro, E. and Ng, S (2020) Latent Dirichlet Analysis of Categorical Survey Responses | 0.965 | 10 | 4 | 90% |
| 3 | Neal, R. M (2012) MCMC using Hamiltonian dynamics | 0.843 | 3 | 3 | 100% |
| 4 | Blei, D. M., Ng, A. Y., and Jordan, M. I (2003) Latent dirichlet allocation | 0.644 | 2 | 2 | 100% |
| 5 | Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., and Stewart, B. M (2018) How to Make Causal Inferences Using Texts | 0.644 | 2 | 2 | 100% |
| 6 | Phan, D., Pradhan, N., and Jankowiak, M (2019) Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro | 0.644 | 2 | 2 | 100% |
| 7 | Griffiths, T. L. and Steyvers, M (2004) Finding scientific topics | 0.511 | 2 | 1 | 100% |
| 8 | Hoffman, M. D. and Gelman, A (2014) The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo | 0.511 | 2 | 1 | 100% |
| 9 | Adams, R. B., Ragunathan, V., and Tumarkin, R (2021) Death by Committee? An Analysis of Corporate Board (Sub-) Committees | 0.405 | 1 | 1 | 100% |
| 10 | Bandiera, O., Fischer, G., Prat, A., and Ytsma, E (2021) Do Women Respond Less to Performance Pay? Building Evidence from Multiple Experiments | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 39 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference for Regression with Variables Generated by AI or Machine Learning | 0.405 | 1 | 1 |