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

Ashton de Silva (), Rob Hyndman () and Ralph Snyder ()

No 3/07, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The vector innovation structural time series framework is proposed as a way of modelling a set of related time series. Like all multi-series approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts. A key feature of the framework is that the series are decomposed into common components such as trend and seasonal effects. Equations that describe the evolution of these components through time are used as the sole way of representing the inter-temporal dependencies. The approach is illustrated on a bivariate data set comprising Australian exchange rates of the UK pound and US dollar. Its forecasting capacity is compared to other common single- and multi-series approaches in an experiment using time series from a large macroeconomic database.

Keywords: Vector innovation structural time series; state space model; multivariate time series; exponential smoothing; forecast comparison; vector autoregression. (search for similar items in EconPapers)
JEL-codes: C32 C51 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ifn
Date: 2007-05
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