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.
Funding source: Community of Madrid, Agencia Estatal de Investigación, Ministry of Economics of Spain, Universidad Francisco Marroquín
Award Identifier / Grant number: MadEco-CM S2015/HUM-3444
Award Identifier / Grant number: 2019/00419/001
Award Identifier / Grant number: ECO2016/00105/001MDM 2014-0431
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: Szabolcs Blazsek and Adrian Licht acknowledge funding from the School of Business of Universidad Francisco Marroquin. Alvaro Escribano acknowledges funding from the Ministry of Economics (ECO2016-00105-001, MDM 2014-0431), the Community of Madrid (MadEco-CM S2015/HUM-3444), and Agencia Estatal de Investigacion (2019/00419/001) of Spain.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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