Marie Corillon, Stephan Smeekes, Ines Wilms
arXiv 2 Jun 2026 · Econometrics
arXiv:2606.03665 · PDF · DOI · OpenAlex · Extracted main text
High-dimensional time series regressions are often regularized to produce sparse coefficients. We show that temporal aggregation provides a powerful alternative to reduce dimensionality in high-order autoregressions and mixed-frequency regressions. To this end, we propose StarTime (Sparse Tree-based Aggregation for Time Series), a convex penalization method that uses a temporal tree to arrange lags hierarchically from high to low frequency. StarTime then flexibly selects coefficients to be aggregated at possibly varying frequencies, sparse or a combination thereof. We provide new error bounds for StarTime, demonstrate improved estimation accuracy and recovery of aggregation and sparsity in simulations relative to benchmarks, and illustrate StarTime's relevance for financial and macroeconomic applications.
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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 | Xiaohan Yan and Jacob Bien (2021) Rare feature selection in high dimensions | 1.000 | 5 | 4 | 100% |
| 2 | Corsi, Fulvio (2009) A simple approximate long-memory model of realized volatility | 0.928 | 4 | 3 | 100% |
| 3 | Adamek, Robert and Smeekes, Stephan and Wilms, Ines (2023) Lasso inference for high-dimensional time series self | 0.894 | 7 | 4 | 71% |
| 4 | Babii, Andrii and Ghysels, Eric and Striaukas, Jonas (2022) Machine learning time series regressions with an application to nowcasting | 0.737 | 3 | 2 | 100% |
| 5 | Jonathan Chassot and Francesco Audrino (2026) HARd to beat: The overlooked impact of rolling windows in the era of machine learning | 0.737 | 3 | 2 | 100% |
| MIDAStouch | unmatched citation key MIDAStouch | 0.737 | 3 | 2 | 100% |
| 7 | Jonas Striaukas and Andrii Babii and Eric Ghysels (2022) midasml: Estimation and prediction methods for high-dimensional mixed frequency time series data | 0.737 | 3 | 2 | 100% |
| 8 | Alain Hecq and Marie Ternes and Ines Wilms (2022) Hierarchical regularizers for mixed-frequency vector autoregressions self | 0.644 | 2 | 2 | 100% |
| 9 | Robert Tibshirani (1996) Regression shrinkage and selection via the lasso | 0.644 | 2 | 2 | 100% |
| 10 | Domenico Giannone and Lucrezia Reichlin and David Small (2008) Nowcasting: The real-time informational content of macroeconomic data | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 70 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.