arXiv 2 Feb 2024 · Econometrics
arXiv:2402.01069 · PDF · DOI · OpenAlex · Extracted main text
Matrix completion estimators are employed in causal panel data models to regulate the rank of the underlying factor model using nuclear norm minimization. This convex optimization problem enables concurrent regularization of a potentially high-dimensional set of covariates to shrink the model size. For valid finite sample inference, we adopt a permutation-based approach and prove its validity for any treatment assignment mechanism. Simulations illustrate the consistency of the proposed estimator in parameter estimation and variable selection. An application to public health policies in Germany demonstrates the data-driven model selection feature on empirical data and finds no effect of travel restrictions on the containment of severe Covid-19 infections.
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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 | S. Athey, M. Bayati, N. Doudchenko, G. Imbens, and K. Khosravi (2021) Matrix completion methods for causal panel data models | 0.916 | 13 | 5 | 77% |
| 2 | V. Chernozhukov, K. Wüthrich, and Y. Zhu (2021) An exact and robust conformal inference method for counterfactual and synthetic controls | 0.851 | 13 | 6 | 62% |
| 3 | T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman (2009) The elements of statistical learning: data mining, inference, and prediction, volume 2 | 0.737 | 3 | 3 | 67% |
| 4 | V. Chernozhukov, C. Hansen, and M. Spindler (2015) Valid post-selection and post-regularization inference: An elementary, general approach | 0.644 | 2 | 2 | 100% |
| 5 | R. Mazumder, T. Hastie, and R. Tibshirani (2010) Spectral regularization algorithms for learning large incomplete matrices | 0.511 | 2 | 2 | 50% |
| 6 | P. R. Rosenbaum and D. B. Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.511 | 2 | 2 | 50% |
| 7 | J. Basseal, C. Bennett, P. Collignon, B. Currie, D. Durrheim, J. Lea… (2023) Key lessons from the covid-19 public health response in australia | 0.511 | 2 | 1 | 100% |
| 8 | Y. Chen and Y. Yang (2021) The one standard error rule for model selection: Does it work? | 0.405 | 1 | 1 | 100% |
| 9 | T. Christensen, M. D. Jensen, M. Kluth, G. H. Kristinsson, K. Lyngga… (2023) The nordic governments' responses to the covid-19 pandemic: A comparative study of variation in governance arrangements and regu… | 0.405 | 1 | 1 | 100% |
| 10 | S. Heiniger (2024) MCMS, 2024 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 57 scored citations.