Detecting Common Bubbles in Multivariate Mixed Causal–Noncausal Models

Gianluca Cubadda, Alain Hecq and Elisa Voisin
Additional contact information
Elisa Voisin: Department of Quantitative Economics, School of Business and Economics, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands

Econometrics, 2023, vol. 11, issue 1, 1-16

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 were investigated in a Monte Carlo study. Finally, we evaluated the practical value of our approach via an empirical application on three commodity prices.

Keywords: forward-looking models; bubbles; comovements (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2225-1146/11/1/9/pdf (application/pdf)
https://www.mdpi.com/2225-1146/11/1/9/ (text/html)

Related works:
Working Paper: Detecting Common Bubbles in Multivariate Mixed Causal-noncausal Models (2023)
Working Paper: Detecting common bubbles in multivariate mixed causal-noncausal models (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:11:y:2023:i:1:p:9-:d:1092261

Access Statistics for this article

Econometrics is currently edited by Ms. Jasmine Liu

More articles in Econometrics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().