Detecting Common Bubbles in Multivariate Mixed Causal-Noncausal Models

G. Cubadda, A. Hecq*, E. Voisin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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 were investigated in a Monte Carlo study. Finally, we evaluated the practical value of our approach via an empirical application on three commodity prices.
Original languageEnglish
Article number9
Number of pages16
Issue number1
Publication statusPublished - 1 Mar 2023

JEL classifications

  • c32 - "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"


  • forward-looking models
  • bubbles
  • comovements


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