Laura Battaglia, Timothy Christensen, Stephen Hansen, Szymon Sacher
arXiv 23 Feb 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2402.15585 · PDF · DOI · OpenAlex · Extracted main text
Researchers now routinely use AI or other machine learning methods to estimate latent variables of economic interest, then plug-in the estimates as covariates in a regression. We show both theoretically and empirically that naively treating AI/ML-generated variables as "data" leads to biased estimates and invalid inference. To restore valid inference, we propose two methods: (1) an explicit bias correction with bias-corrected confidence intervals, and (2) joint estimation of the regression parameters and latent variables. We illustrate these ideas through applications involving label imputation, dimensionality reduction, and index construction via classification and aggregation.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Baker, S. R., Bloom, N., and Davis, S. J (2016) Measuring Economic Policy Uncertainty | 1.000 | 7 | 3 | 100% |
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| 3 | Gorodnichenko, Y., Pham, T., and Talavera, O (2023) The Voice of Monetary Policy | 0.874 | 9 | 2 | 100% |
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| 6 | Ke, Z. T. and Wang, M (2022) Using SVD for Topic Modeling | 0.737 | 4 | 3 | 50% |
| 7 | Wu, R., Zhang, L., and Tony Cai, T (2023) Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference | 0.737 | 4 | 3 | 50% |
| 8 | Gürkaynak, R. S., Sack, B., and Swanson, E (2005) Do actions speak louder than words? The response of asset prices to monetary policy actions and statements | 0.737 | 3 | 2 | 100% |
| 9 | Allon, G., Chen, D., Jiang, Z., and Zhang, D (2023) Machine Learning and Prediction Errors in Causal Inference | 0.644 | 2 | 2 | 100% |
| 10 | Ash, E., Morelli, M., and Vannoni, M (2025) More Laws, More Growth? Evidence from U.S. States | 0.644 | 2 | 2 | 100% |
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