Publication: Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production
Loading...
Identifiers
Files
Publication date
2019-10
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
In this paper, new Seasonal-QVAR (quasi-vector autoregressive) and Markov switching (MS) Seasonal-QVAR (MS-Seasonal-QVAR) models are introduced. Seasonal-QVAR is an outlierrobust score-driven state space model, which is an alternative to classical multivariate Gaussian models (e.g. basic structural model; Seasonal-VARMA). Conditions of the maximum likelihood estimator and impulse response functions are shown. Dynamic relationships between world crude oil production and US industrial production are studied for the period of 1973 to 2019. Statistical performances of alternative models are analyzed. MS-Seasonal-QVAR identies structural changes and extreme observations in the dataset. MS-Seasonal-QVAR is superior to Seasonal-QVAR and, and both are superior to Gaussian alternatives.
Description
Keywords
World Crude Oil Production, United States Industrial Production, Dynamic Conditional, Score Models, Score-Driven Multivariate Stochastic Location And Stochastic Seasonality Models, Markov Regime-Switching Models