Alain Hecq, Marie Ternes, Ines Wilms
arXiv 23 Feb 2021 · Econometrics · publishedJournal of Computational and Graphical Statistics (2022) · 3 citations (OpenAlex)
arXiv:2102.11780 · PDF · DOI · OpenAlex · Extracted main text
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per low-frequency period grow, MF-VARs suffer from the "curse of dimensionality". We curb this curse through a regularizer that permits hierarchical sparsity patterns by prioritizing the inclusion of coefficients according to the recency of the information they contain. Additionally, we investigate the presence of nowcasting relations by sparsely estimating the MF-VAR error covariance matrix. We study predictive Granger causality relations in a MF-VAR for the U.S. economy and construct a coincident indicator of GDP growth. Supplementary Materials for this article are available online.
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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 | Cimadomo, J., D. Giannone, M. Lenza, F. Monti, and A. Sokol (2021) Nowcasting with large Bayesian vector autoregressions | 1.000 | 7 | 4 | 100% |
| 2 | Ghysels, E (2016) Macroeconomics and the reality of mixed frequency data | 0.874 | 5 | 2 | 100% |
| 3 | Götz, T. B., A. Hecq, and S. Smeekes (2016) Testing for Granger causality in large mixed-frequency VARs | 0.843 | 3 | 3 | 100% |
| 4 | Babii, A., E. Ghysels, and J. Striaukas (2021) Machine learning time series regressions with an application to nowcasting | 0.811 | 4 | 2 | 100% |
| 5 | Götz, T. B. and A. Hecq (2014) Nowcasting causality in mixed frequency vector autoregressive models | 0.737 | 3 | 2 | 100% |
| 6 | Nicholson, W. B., I. Wilms, J. Bien, and D. S. Matteson (2020) High dimensional forecasting via interpretable vector autoregression | 0.737 | 3 | 2 | 100% |
| 7 | Paccagnini, A. and F. Parla (2021) Identifying high-frequency shocks with Bayesian mixed-frequency VARs | 0.737 | 3 | 2 | 100% |
| 8 | Derimer, M., F. X. Diebold, L. Liu, and K. Yilmaz (2018) Estimating Global Bank Network Connectedness | 0.644 | 2 | 2 | 100% |
| 9 | Hsu, N.-J., H.-L. Hung, and Y.-M. Chang (2008) Subset selection for vector autoregressive processes using lasso | 0.644 | 2 | 2 | 100% |
| 10 | McCracken, M. and S. Ng (2016) FRED-MD: a monthly database for macroeconomic research | 0.585 | 4 | 1 | 75% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions | 0.874 | 6 | 2 |
| 2 | Sparse Tree-Based Aggregation for Time Series Regressions | 0.644 | 2 | 2 |
| 3 | Sparse Temporal Disaggregation | 0.405 | 1 | 1 |