2017/11/11 by Philip G. Sansom, Sansom, Philip G., Daniel B. Williamson +4
Economics, Econometrics and Finance · Environmental Science · Mathematics · #Applications (stat.AP) #Climate variability and models #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Hydrology and Drought Analysis #stat.AP
paper · pdf · doi:10.48550/arxiv.1711.04135
27 pages, 8 figures, 4 tables
openalex publication_date 2017/11/11 · arxiv created 2019/02/06 · arxiv updated 2019/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure of an observed time series due to intermittent coupling with an unobserved process. Novel intervention methods are proposed to model the effect of repeated coupling as a single dynamic process. Latent time-varying autoregressive components are developed to model changes in the temporal correlation structure. Efficient filtering and smoothing methods are derived for the resulting class of models. We propose methods for quantifying the component of variance attributable to an unobserved process, the effect during individual coupling events, and the potential for skilful forecasts. The proposed methodology is applied to the study of winter-time variability in the dominant pattern of climate variation in the northern hemisphere, the North Atlantic Oscillation. Around 70% of the inter-annual variance in the winter (Dec-Jan-Feb) mean level is attributable to an unobserved process. Skilful forecasts for winter (Dec-Jan-Feb) mean are possible from the beginning of December.