Statistical Modelling 10 (2010), 353374
The vector innovations structural time series framework:
a simple approach to multivariate forecasting
Ashton de Silva
School of Economics, Finance and Marketing,
RMIT University,
VIC 300
Australia
eMail: ashton.desilva@rmit.edu.au
Rob J Hyndman and Ralph Snyder
Department of Econometrics and Business Statistics,
Monash University
Australia
Abstract:
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 interseries 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.
Keywords:
exponential smoothing; forecast comparison; multivariate time series;
state space model;vector autoregression;
vector innovations structural time series model
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