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Detecting Common Bubbles in Multivariate Mixed Causal-noncausal Models

Gianluca Cubadda, Alain Hecq and Elisa Voisin ()
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Elisa Voisin: Maastricht University

No 555, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: 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 suggest statistical tools to detect the common locally explosive dynamics in a Student-t distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria are investigated in a Monte Carlo study. Finally, we evaluate the practical value of our approach by an empirical application on three commodity prices.

Keywords: Forward-looking models; bubbles; co-movements (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2023-02-27, Revised 2023-02-27
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Related works:
Journal Article: Detecting Common Bubbles in Multivariate Mixed Causal–Noncausal Models (2023) Downloads
Working Paper: Detecting common bubbles in multivariate mixed causal-noncausal models (2022) Downloads
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