Detecting Common Bubbles in Multivariate Mixed Causal–Noncausal Models

This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established conditions under which bubbles present within class of mixed causal–noncausal vector autoregressive models, we suggest statistical tools detect locally explosive dynamics a Student t-distribution maximum likelihood framework. The performances both ratio tests information criteria were investigated Monte Carlo study. Finally, evaluated practical value our approach via an empirical application on three commodity prices.