Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
| العنوان: | Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions |
|---|---|
| المؤلفون: | Alain Hecq, Marie Ternes, Ines Wilms |
| المساهمون: | QE Econometrics, RS: GSBE Theme Data-Driven Decision-Making, RS: GSBE Theme Learning and Work, RS: FSE DACS Mathematics Centre Maastricht, RS: GSBE other - not theme-related research |
| المصدر: | Journal of Computational and Graphical Statistics, 31(4), 1076-1090. Taylor and Francis |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Statistics and Probability, SELECTION, Variable selection, Econometrics (econ.EM), Coincident indicators, GRANGER CAUSALITY, GDP, Methodology (stat.ME), FOS: Economics and business, Group lasso, Discrete Mathematics and Combinatorics, INFERENCE, High-dimensionality, Statistics, Probability and Uncertainty, MIDAS, Statistics - Methodology, Economics - Econometrics |
| الوصف: | 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. Forthcoming in Journal of Computational and Graphical Statistics |
| اللغة: | English |
| تدمد: | 1061-8600 |
| DOI: | 10.1080/10618600.2022.2058003 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1829b0c4e5f9b9e909630bc2fdbeac37 https://doi.org/10.1080/10618600.2022.2058003 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....1829b0c4e5f9b9e909630bc2fdbeac37 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10618600 |
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| DOI: | 10.1080/10618600.2022.2058003 |