This paper introduces the notion of common noncausal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co-movements might not be detected using the conventional stationary vector autoregressive (VAR) model as the common dynamics are...

This paper proposes an econometric framework to assess the importance of common shocks and common transmission mechanisms ingenerating international business cycles. Then we show how to decompose the cyclical effects of permanent-transitory shocks into those due to their domestic and those due...

This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our variables are characterized by the presence of long range...

Combining economic time series with the aim to obtain an indicator for business cycle analyses is an important issue for policy makers. In this area, econometric techniques usually rely on systems with either a small number of series, N, (VAR or VECM) or, at the other extreme, a very large N...

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....

This paper introduces a new modelling for detecting the presence of commonalities in a set of realized volatility measures. In particular, we propose a multivariate generalization of the heterogeneous autoregressive model (HAR) that is endowed with a common index structure. The Vector...

This paper proposes a strategy to detect the presence of common serial correlation in high-dimensional systems. We show by simulations that univariate autocorrelation tests on the factors obtained by partial least squares outperform traditional tests based on canonical correlations

This chapter surveys the importance of reduced rank regression techniques (RRR) for modelling economic and financial time series. We mainly focus on models that are capable to reproduce the presence of common dynamics among variables such as the serial correlation common feature and the...

This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal-noncausal vector autoregressive models, we...

This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common components generates the entire dynamics of the large...