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Detecting Common Bubbles in Multivariate Mixed Causal-Noncausal Models
Gianluca Cubadda
Alain W. J. Hecq
Elisa Voisin
出版
CEIS Tor Vergata
, 2023
URL
http://books.google.com.hk/books?id=1bXjzwEACAAJ&hl=&source=gbs_api
註釋
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.