Detecting Co-Movements in Noncausal Time Series
Gianluca Cubadda (),
Alain Hecq () and
Sean Telg ()
No 430, CEIS Research Paper from Tor Vergata University, CEIS
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 present in the noncausal (i.e. forward-looking) component of the series. In particular, we show that the presence of a reduced rank structure allows to identify purely causal and noncausal VAR processes of order two and higher even in the Gaussian likelihood framework. Hence, usual test statistics and canonical correlation analysis can still be applied, where both lags and leads are used as instruments to determine whether the common features are present in either the backward-or forward-looking dynamics of the series. The proposed definitions of co-movements also valid for the mixed causal-noncausal VAR, with the exception that an approximate non-Gaussian maximum likelihood estimator is necessary for these cases. This means however that one loses the benefits of the simple tools proposed in this paper. An empirical analysis on European Brent and U.S. West Texas Intermediate oil prices illustrates the main findings. Whereas we fail to find any short run co-movements in a conventional causal VAR, they are detected in the growth rates of the series when considering a purely noncausal VAR.
Keywords: causal and noncausal process; common features; vector autoregressive models; oil prices (search for similar items in EconPapers)
JEL-codes: C12 C32 E32 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2018-04-23, Revised 2018-04-23
New Economics Papers: this item is included in nep-ene, nep-ets and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
ftp://www.ceistorvergata.it/repec/rpaper/RP430.pdf Main text (application/pdf)
Our link check indicates that this URL is bad, the error code is: 500 Failed to connect to FTP server www.ceistorvergata.it: [Net::FTP] Connection closed
Journal Article: Detecting Co‐Movements in Non‐Causal Time Series (2019)
Working Paper: Detecting Co-Movements in Noncausal Time Series (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rtv:ceisrp:430
Ordering information: This working paper can be ordered from
CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
Access Statistics for this paper
More papers in CEIS Research Paper from Tor Vergata University, CEIS CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma. Contact information at EDIRC.
Bibliographic data for series maintained by Barbara Piazzi ().