This note argues that large VAR models with common cyclical feature restrictions provide an attractive framework for parsimonious implied univariate final equations, justifying on the one hand the estimation of homogenous panels with dynamic heterogeneity and a common factor structure, and on...

For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-moving average (ARIMA hereafter) models....

VAR models with common cyclical features imply parsimonious univariate final equations, justifying the use of both time series aggregation, and homogenous panels with common factors and dynamic heterogeneity. However, conducting statistical inferences based on too restrictive models might be...

For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-integrated-moving average (ARIMA...

We propose an approach for checking the data admissibility of non-stationary multivariate time series models (VAR or VARMA) through that of their implied individual ARIMA specifications. In particular we show that the presence of different kinds of common cyclical features restrictions, leading...

This note argues that large VAR models with common cyclical feature restrictions provide an attractive framework for parsimonious implied univariate final equations, justifying on the one hand the estimation of homogenous panels with dynamic heterogeneity and a common factor structure, and on...