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Detecting Co-Movements in Noncausal Time Series

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  • Cubadda, Gianluca
  • Hecq, Alain
  • Telg, Sean

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.

Suggested Citation

  • Cubadda, Gianluca & Hecq, Alain & Telg, Sean, 2017. "Detecting Co-Movements in Noncausal Time Series," MPRA Paper 77254, University Library of Munich, Germany, revised 02 Mar 2017.
  • Handle: RePEc:pra:mprapa:77254
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    References listed on IDEAS

    as
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    Cited by:

    1. Alain Hecq & Elisa Voisin, 2019. "Predicting bubble bursts in oil prices during the COVID-19 pandemic with mixed causal-noncausal models," Papers 1911.10916, arXiv.org, revised Mar 2021.

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    More about this item

    Keywords

    mixed causal-noncausal process; common features; vector autoregressive models; commodity prices; common bubbles.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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