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

Author

Listed:
  • Blazsek Szabolcs

    (Universidad Francisco Marroquín, School of Business, Guatemala City, Guatemala)

  • Escribano Alvaro

    (Universidad Carlos III de Madrid, Department of Economics, Madrid 126, Getafe, 28903, Madrid, Spain)

  • Licht Adrian

    (Universidad Francisco Marroquín, School of Business, Guatemala City, Guatemala)

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.

Suggested Citation

  • Blazsek Szabolcs & Escribano Alvaro & Licht Adrian, 2022. "Multivariate Markov-switching score-driven models: an application to the global crude oil market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(3), pages 313-335, June.
    • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:3:p:313-335:n:7
    DOI: 10.1515/snde-2020-0099
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    Cited by:

    1. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.

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