The vector innovations structural time series framework: a simple approach to multivariate forecasting

被引:15
作者
de Silva, Ashton [1 ]
Hyndman, Rob J. [2 ]
Snyder, Ralph [2 ]
机构
[1] RMIT Univ, Sch Econ Finance & Mkt, Melbourne, Vic 3000, Australia
[2] Monash Univ, Dept Econometr & Business Stat, Clayton, Vic 3800, Australia
关键词
exponential smoothing; forecast comparison; multivariate time series; state space model; vector autoregression; vector innovations structural time series model; MODELS;
D O I
10.1177/1471082X0901000401
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The vector innovations structural time series framework is proposed as a way of modelling a set of related time series. As with all multivariate approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts. The model is based around an unobserved vector of components representing features such as the level and slope of each time series. Equations that describe the evolution of these components through time are used to represent the inter-temporal dependencies. The approach is illustrated on a bivariate dataset comprising Australian exchange rates of the UK pound and US dollar. The forecasting accuracy of the new modelling framework is compared to other common uni- and multivariate approaches in an experiment using time series from a large macroeconomic database.
引用
收藏
页码:353 / 374
页数:22
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