249 – Regularization and Prediction in Time Series and Longitudinal Models
Estimation of Multi-Granger Network Causal Models
Andrey Skripnikov
University of Florida
George Michailidis
University of Florida
Network Granger causality focuses on estimating Granger causal effects from multivariate time series and it can be operationalized through Vector Autoregressive Models (VAR). The latter represent a popular class of time series models that has been widely used in applied econometrics and finance and more recently in biomedical applications. In this work, we discuss joint estimation and model selection issues of multiple Granger causal networks. We present a modeling framework for the setting where the same variables are measured on different entities (e.g. same set of economic activity variables for related countries). The framework involves the introduction of appropriate structural penalties on the transition matrices of the respective VAR models that link the underlying network Granger models and use of factor modeling for error covariance estimation. ADMM algorithm is presented for implementation of joint optimization procedure and the model is evaluated on both synthetic and real data.