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Multivariate Markov-switching score-driven models: an application to the global crude oil market

Szabolcs Blazsek (), Alvaro Escribano () and Licht Adrian ()
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Licht Adrian: Universidad Francisco Marroquín, School of Business, Guatemala City, Guatemala

Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 3, 313-335

Abstract: A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

Keywords: global crude oil market; markov regime-switching models; nonlinear co-integration; score-driven models; structural changes (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/snde-2020-0099

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