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Detecting Co-Movements in Noncausal Time Series

Gianluca Cubadda, Alain Hecq and Sean Telg

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces the notion of common noncausal features and proposes tools for detecting the presence of co-movements in economic and financial time series subject to phenomena such as asymmetric cycles and speculative bubbles. For purely causal or noncausal vector autoregressive models with more than one lag, the presence of a reduced rank structure allows to identify causal from noncausal systems using the usual Gaussian likelihood framework. This result cannot be extended to mixed causal-noncausal models, and an approximate maximum likelihood estimator assuming non-Gaussian disturbances is needed for this case. We find common bubbles in both commodity prices and price indicators.

Keywords: mixed causal-noncausal process; common features; vector autoregressive models; commodity prices; common bubbles. (search for similar items in EconPapers)
JEL-codes: C12 C32 E32 (search for similar items in EconPapers)
Date: 2017-03-02, Revised 2017-03-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
References: View references in EconPapers View complete reference list from CitEc

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Related works:
Journal Article: Detecting Co‐Movements in Non‐Causal Time Series (2019) Downloads
Working Paper: Detecting Co-Movements in Noncausal Time Series (2018) Downloads
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