Alain Hecq, Marie Ternes, Ines Wilms
arXiv 25 Jan 2023 · Econometrics · publishedJournal of Forecasting (2025)
arXiv:2301.10592 · PDF · DOI · OpenAlex · Extracted main text
Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structure of RU-MIDAS regressions, the dimensionality grows quickly if the frequency mismatch between the high- and low-frequency variables is large. Additionally the number of high-frequency observations available for estimation decreases. We propose to counteract this reduction in sample size by pooling the high-frequency coefficients and further reduce the dimensionality through a sparsity-inducing convex regularizer that accounts for the temporal ordering among the different lags. To this end, the regularizer prioritizes the inclusion of lagged coefficients according to the recency of the information they contain. We demonstrate the proposed method on two empirical applications, one on realized volatility forecasting with macroeconomic data and another on demand forecasting for a bicycle-sharing system with ridership data on other transportation types.
appendix boundary found by appendix_command · 88% of the source is main text. Read the extracted text to check this.
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 | Foroni, C., P. Guérin, and M. Marcellino (2018) Using low frequency information for predicting high frequency variables | 0.874 | 7 | 2 | 100% |
| 2 | Hecq, A., M. Ternes, and I. Wilms (2022) Hierarchical regularizers for mixed-frequency vector autoregressions self | 0.874 | 6 | 2 | 100% |
| 3 | Conrad, C. and K. Loch (2015) Anticipating long-term stock market volatility | 0.811 | 4 | 2 | 100% |
| 4 | Fang, T., T.-H. Lee, and Z. Su (2020) Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection | 0.811 | 4 | 2 | 100% |
| 5 | Ghysels, E (2016) Macroeconomics and the reality of mixed frequency data | 0.811 | 4 | 2 | 100% |
| 6 | Babii, A., E. Ghysels, and J. Striaukas (2022) Machine learning time series regressions with an application to nowcasting | 0.737 | 3 | 2 | 100% |
| 7 | Engle, R. F., E. Ghysels, and B. Sohn (2013) Stock market volatility and macroeconomic fundamentals | 0.737 | 3 | 2 | 100% |
| 8 | Götz, T. B., A. Hecq, and S. Smeekes (2016) Testing for Granger causality in large mixed-frequency VARs | 0.737 | 3 | 2 | 100% |
| 9 | Asgharian, H., A. J. Hou, and F. Javed (2013) The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach | 0.644 | 2 | 2 | 100% |
| 10 | Conrad, C. and O. Kleen (2020) Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models | 0.644 | 2 | 2 | 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 | Econometrics of Machine Learning Methods in Economic Forecasting | 0.405 | 1 | 1 |