Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

We present the R-package mgm for the estimation of k-order Mixed Graphical Models
(MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These …

The vector autoregression (VAR) has long proven to be an effective method for modeling the
joint dynamics of macroeconomic time series, as well as for forecasting. One major …

We develop a framework for forecasting multivariate data that follow known linear
constraints. This is particularly common in forecasting where some variables are aggregates …

We examine the high-frequency return and volatility of major cryptocurrencies and reveal
that spillovers among them exist. Our analysis shows that return and volatility clustering …

Vector autoregression (VAR) models are widely used for multivariate time series analysis in
macroeconomics, finance, and related fields. Bayesian methods are often employed to deal …

Identifying directed interactions between species from time series of their population
densities has many uses in ecology. This key statistical task is equivalent to causal time …

Abstract The ability of Google Trends data to forecast the number of new daily cases and
deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the …

Human microbiome research is rife with studies attempting to deduce microbial correlation
networks from sequencing data. Standard correlation and/or network analyses may be …

Matrix-variate time series are now common in economic, medical, environmental, and
atmospheric sciences, typically associated with large matrix dimensions. We introduce a …