arXiv 23 Jun 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2026) · 20 citations (OpenAlex)
arXiv:2306.13362 · PDF · DOI · OpenAlex · Extracted main text
This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification, $\tau$-mixing dependence, and polynomial tails. Our method's finite sample performance is assessed via Monte Carlo simulations. We apply the methodology to nowcasting U.S. GDP growth and demonstrate that it outperforms both sparse regression and standard factor-augmented regression during the COVID-19 pandemic. To ensure the robustness of these results, we also implement factor-augmented sparse logistic regression, which further confirms the superior accuracy of our nowcast probabilities during recessions. These findings indicate that recessions are influenced by both idiosyncratic (sparse) and common (dense) shocks.
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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 | Babii, Ghysels \ Striaukas (2022) `Machine learning time series regressions with an application to nowcasting', Journal of Business & Economic Statistics 40(3), 1… | 1.000 | 14 | 4 | 100% |
| 2 | Ahn \ Horenstein (2013) `Eigenvalue ratio test for the number of factors', Econometrica 81(3), 1203–1227 | 0.928 | 4 | 3 | 100% |
| 3 | Bickel, Ritov \ Tsybakov (2009) `Simultaneous analysis of Lasso and Dantzig selector', Annals of Statistics 37(4), 1705 – 1732 | 0.874 | 5 | 2 | 100% |
| 4 | Fan, Masini \ Medeiros (2023) `Bridging factor and sparse models', Annals of Statistics 51(4), 1692–1717 | 0.811 | 4 | 2 | 100% |
| 5 | Beyhum \ Striaukas (2024) `Testing for sparse idiosyncratic components in factor-augmented regression models', Journal of Econometrics 244(1), 105845 | 0.737 | 3 | 2 | 100% |
| 6 | Dedecker \ Prieur (2004) `Coupling for $$-dependent sequences and applications', Journal of Theoretical Probability 17(4), 861–885 | 0.737 | 3 | 2 | 100% |
| 7 | Diebold (2020) Real-time real economic activity: Exiting the great recession and entering the pandemic recession, Working Paper 27482, National… | 0.737 | 3 | 2 | 100% |
| 8 | Bai \ Ng (2002) `Determining the number of factors in approximate factor models', Econometrica 70(1), 191–221 | 0.644 | 2 | 2 | 100% |
| 9 | Bai \ Ng (2019) `Rank regularized estimation of approximate factor models', Journal of Econometrics 212(1), 78–96 | 0.644 | 2 | 2 | 100% |
| 10 | Bai (2003) `Inferential theory for factor models of large dimensions', Econometrica 71(1), 135–171 | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Factor-Augmented Machine Learning Panel Regressions | 0.811 | 4 | 2 |
| 2 | Diffusion Index Forecasting with Tensor Data | 0.405 | 1 | 1 |