Factor-Augmented Sparse Midas Regressions with an Application to Nowcasting
Bibliographic Data
| ID | 21558044 |
|---|---|
| Authors | Jad Beyhum (0000-0001-6849-8026, KU Leuven), Jonas Striaukas (0000-0001-7658-8638, Copenhagen Business School, corresponding author) |
| Year | 2026 |
| Pages | 1-11 |
| Publication date | 2026-07-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2026.2649055 |
| OpenAlex | W7167592463 |
| Language | EN |
| References cited | 45 |
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 due to the MIDAS approximation error, τ-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. These findings indicate that the growth through this period was influenced by both idiosyncratic (sparse) and common (dense) shocks. The approach is implemented in the midasml R package, available on CRAN
Curse of dimensionality · Dimensionality reduction · Estimator · Monte Carlo method · Nowcasting · Polynomial · Polynomial regression · Regression · Complex Systems and Time Series Analysis · Monetary Policy and Economic Impact · Statistical Methods and Inference
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| Highly cited | No |
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