The study considers a vector autoregressive model for high-dimensional mixed fre- quency data, where selective time series are collected at different frequencies. The high-frequency series are expanded and modeled as multiple time series to match the …
The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patterns are …
Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional …