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Detecting common bubbles in multivariate mixed causal-noncausal models

Gianluca Cubadda, Alain Hecq, Elisa Voisin

arXiv 23 Jul 2022 · Econometrics · publishedEconometrics (2023) · 7 citations (OpenAlex)

arXiv:2207.11557 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes methods to investigate whether the bubble patterns observed in individual series are common to various series. We detect the non-linear dynamics using the recent mixed causal and noncausal models. Both a likelihood ratio test and information criteria are investigated, the former having better performances in our Monte Carlo simulations. Implementing our approach on three commodity prices we do not find evidence of commonalities although some series look very similar.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Cubadda, G., Hecq, A (2001) On non-contemporaneous short-run co-movements self0.40511100%

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
12504.186780.64422
2Optimization of the Generalized Covariance Estimator in Noncausal Processes0.51121
32501.039450.40511
42509.134920.40511