arXiv 31 Dec 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2301.00292 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels with many covariates by combining post-selection inference with a novel multiple testing adjustment. Our data-driven hypotheses are conditional on the sparse covariate selection. We control for family-wise error rates for covariate discovery for large cross-sections. As an easy-to-use and practically relevant procedure, we propose Panel-PoSI, which combines the data-driven adjustment for panel multiple testing with valid post-selection p-values of a generalized LASSO, that allows us to incorporate priors. In an empirical study, we select a small number of asset pricing factors that explain a large cross-section of investment strategies. Our method dominates the benchmarks out-of-sample due to its better size and power.
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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 | Choi, Taylor, and Tibshirani (2017) Selecting the number of principal components: Estimation of the true rank of a noisy matrix | 1.000 | 5 | 3 | 100% |
| 2 | Tian and Taylor (2017) Asymptotics of selective inference | 0.737 | 5 | 3 | 40% |
| 3 | G'Sell, Wager, Chouldechova, and Tibshirani (2016) Sequential selection procedures and false discovery rate control | 0.737 | 3 | 2 | 100% |
| 4 | Lee, Sun, Sun, and Taylor (2016) Exact post-selection inference, with application to the lasso | 0.659 | 7 | 3 | 29% |
| 5 | Simes (1986) An Improved Bonferroni Procedure for Multiple Tests of Significance | 0.644 | 2 | 2 | 100% |
| 6 | Bonferroni (1935) Il calcolo delle assicurazioni su gruppi di teste | 0.585 | 3 | 1 | 100% |
| 7 | Tian, Loftus, and Taylor (2018) Selective inference with unknown variance via the square-root lasso | 0.511 | 4 | 2 | 25% |
| 8 | Javanmard and Montanari (2018) Debiasing the lasso: optimal sample size for Gaussian designs | 0.511 | 3 | 2 | 33% |
| 9 | Belloni and Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models | 0.511 | 3 | 2 | 33% |
| 10 | Chatterjee (2014) Assumptionless consistency of the Lasso | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 46 scored citations.