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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.
|
Anderson B and Moore JB (1979) Optimal filtering. New Jersey: Prentice-Hall . Google Scholar | |
|
Ansley CF and Kohn R (1985) Estimation, filtering and smoothing in state space models with incompletely specified conditions . Annals of Statistics, 13, 1286–316 . Google Scholar | Crossref | ISI | |
|
Armstrong JS (2001) Should we redesign forecasting competitions? International Journal of Forecasting, 17, 542–45 . Google Scholar | ISI | |
|
Athanasopoulos G and Vahid F (2008) VARMA versus VAR for macroeconomic forecasting . Journal of Economic and Business Statistics, 26, 237–52 . Google Scholar | Crossref | ISI | |
|
Billah B , King M , Snyder R and Koehler A (2006) Exponential smoothing model selection for forecasting . International Journal of Forecasting, 22, 239–47 . Google Scholar | Crossref | ISI | |
|
Box GEP and Jenkins G (1970) Time series analysis: forecasting and control. San Francisco: Holden-Day . Google Scholar | |
|
Brockwell PJ and Davis RA (1991) Time series: theory and methods, 2nd edn. New York: Springer-Verlag . Google Scholar | Crossref | |
|
Brown RG (1959) Statistical forecasting for inventory control. New York: McGraw-Hill . Google Scholar | |
|
Clarida R , Sarno L , Taylor M and Valente G (2003) The out-of-sample success of term structure models as exchange rate predictors: one step along . Journal of International Economics, 60, 61–83 . Google Scholar | Crossref | ISI | |
|
de Jong P (1991) The diffuse Kalman Filter . Annals of Statistics, 19, 1073–83 . Google Scholar | Crossref | ISI | |
|
Dickey D and Fuller W (1979) Distribution of the estimates for autoregressive time series with a unit root . Journal of American Statistical Association, 74, 427–31 . Google Scholar | ISI | |
|
Durbin J and Koopman S (2001) Time series analysis by state space method. Oxford: Oxford University Press . Google Scholar | |
|
Enns PG , Machak JA , Spivey A and Wrobleski WJ (1982) Forecasting applications of an adaptive multiple exponential smoothing model . Management Science, 28, 1035–44 . Google Scholar | Crossref | ISI | |
|
Gardner ES and McKenzie E (1985) Forecasting trends in time series . Management Science, 31, 1237–46 . Google Scholar | Crossref | ISI | |
|
Granger C (2001) Comments on the M3 forecast evaluation and a comparison with a study by Stock and Watson . International Journal of Forecastings, 17, 565–67 . Google Scholar | ISI | |
|
Granger CW and Newbold P (1986) Forecasting economic time series, 2nd edn. New York: Academic Press . Google Scholar | |
|
Hamilton JD (1994) Time series analysis. Princeton: Princeton University Press . Google Scholar | |
|
Hannan EJ (1969) The identification of vector mixed Autoregressive-Moving Average systems . Biometrica 56, 223–25 . Google Scholar | ISI | |
|
Hannan EJ and Deistler M (1988) The statistical theory of linear systems. New York: John Wiley & Sons . Google Scholar | |
|
Harvey A (1986) Analysis and generalisation of a multivariate exponential smoothing model . Management Science, 32, 374–80 . Google Scholar | Crossref | ISI | |
|
Harvey A and Koopman S (1997) Multivariate structural time series model. In Heij C , Schumacher H and Hanzon B (eds) System dynamics in economic and financial models. Chichester: John Wiley and Sons , 269–96. Google Scholar | |
|
Harvey AC (1989) Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press . Google Scholar | |
|
Hyndman R and Koehler AB (2006) Another look at measures of forecast accuracy . International Journal of Forecasting, 22, 679–88 . Google Scholar | Crossref | ISI | |
|
Hyndman RJ (2001) It’s time to move from ‘what’ to ‘why’ . International Journal of Forecasting, 17, 567–70 . Google Scholar | ISI | |
|
Hyndman RJ , Koehler AB , Ord JK and Snyder RD (2008) Forecasting with exponential smoothing: the state space approach. Berlin: Springer . Google Scholar | Crossref | |
|
Hyndman RJ , Koehler AB , Snyder RD and Grose S (2002) A state space framework for automatic forecasting using exponential smoothing methods . International Journal of Forecasting, 18, 439–54 . Google Scholar | Crossref | ISI | |
|
Jones RH (1966) Exponential smoothing for multivariate time series . Journal of the Royal Statistical Society, Series B, 28, 241–51 . Google Scholar | |
|
Kwiatkowski D , Phillips PCB , Schmidt P and Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root . Journal of Econometrics, 54, 159–78 . Google Scholar | Crossref | ISI | |
|
Lütkepohl H (2005) New introduction to multiple time series analysis. Berlin: Springer-Verlag . Google Scholar | Crossref | |
|
Makridakis M and Hibon M (2000) The M3-competition: results, conclusions and implications . International Journal of Forecasting, 16, 451–76 . Google Scholar | Crossref | ISI | |
|
Meese R and Rognoff K (1983) Empirical exchange rate model of the seventies . Journal of International Economics, 14, 3–24 . Google Scholar | Crossref | ISI | |
|
Ord JK , Koehler AB and Snyder RD (1997) Estimation and prediction for a class of dynamic nonlinear statistical models . Journal of the American Statistical Association, 92, 1621–29 . Google Scholar | Crossref | ISI | |
|
Pfeffermann D and Allon J (1989) Multivariate exponential smoothing: method and practice . International Journal of Forecasting, 5, 83–98 . Google Scholar | Crossref | ISI | |
|
Sims C (1980) Macroeconomics and reality . Econometrica, 48, 1–49 . Google Scholar | Crossref | ISI | |
|
Snyder RD (1985) Recursive estimation of dynamic linear statistical models . Journal of the Royal Statistical Society, Series B, 47, 272–76 . Google Scholar | |
|
Tiao G and Tsay R (1989) Model specification in multivariate time series . Journal of the Royal Statistical Society, Series B, 51, 157–213 . Google Scholar | |
|
Watson M (2003) Macroeconomic forecasting using many predictors. In Dewatripont M , Hansen P and Turnovsky SJ eds. Advances in economics and econometrics, theory and applications, eighth world congress of the econometric society, 3. USA: Cambridge University Press , 87–115. Google Scholar |
