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EnglishWe model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out-of-sample forecasting.
| Titolo: | NETS: Network Estimation for Time Series |
| Autore/i: | Barigozzi M; Brownlees C |
| Autore/i Unibo: | |
| Anno: | 2019 |
| Rivista: | |
| Digital Object Identifier (DOI): | https://doi.org/10.1002/jae.2676 |
| Abstract: | We model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out-of-sample forecasting. |
| Data stato definitivo: | 2020-02-28T19:57:09Z |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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