Algorithm

World Wide Web

Volatility (finance)

Biology

Vector autoregression

Statistics

Covariance matrix

Applied mathematics

Time series

Theoretical computer science

Economics

Computer science

STAR model

Geology

Paleontology

Covariance

Estimation

Graph

Mathematics

Politics

Management

Autoregressive model

Political science

Econometrics

Autoregressive integrated moving average

Series (stratigraphy)

Representation (politics)

Lasso (programming language)

Combinatorics

Machine learning

Journal of Applied Econometrics

Summary 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.

NETS: Network estimation for time series