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

Statistical Modelling

Volumn 10, Issue 4, 2010, Pages 353-374

  de Silva, Ashton ^a   Hyndman, Rob J ^b   Snyder, Ralph ^b  

^a RMIT UNIVERSITY   (Australia)

^b MONASH UNIVERSITY   (Australia)

Author keywords

exponential smoothing; forecast comparison; multivariate time series; state space model; vector autoregression; vector innovations structural time series model

Indexed keywords

EID: 78751495093     PISSN: 1471082X     EISSN: 14770342     Source Type: Journal    
DOI: 10.1177/1471082X0901000401     Document Type: Article

References (37)

1. Anderson B and Moore JB (1979) Optimal filtering. New Jersey: Prentice-Hall.
2. Ansley CF and Kohn R (1985) Estimation, filtering and smoothing in state space models with incompletely specified conditions. Annals of Statistics, 13, 1286-316.
3. Armstrong JS (2001) Should we redesign forecasting competitions?International Journal of Forecasting, 17, 542-45.
4. Athanasopoulos G and Vahid F (2008) VARMA versus VAR for macroeconomic forecasting. Journal of Economic and Business Statistics, 26, 237-52.
5. Billah B, King M, Snyder R and Koehler A (2006) Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22, 239-47.
6. Box GEP and Jenkins G (1970) Time series analysis: forecasting and control. San Francisco: Holden-Day.
7. Brockwell PJ and Davis RA (1991) Time series: theory and methods, 2nd edn.New York: Springer-Verlag.
8. Brown RG (1959) Statistical forecasting for inventory control. New York: McGraw-Hill.
9. 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.
10. de Jong P (1991) The diffuse Kalman Filter. Annals of Statistics, 19, 1073-83.
11. 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.
12. Durbin J and Koopman S (2001) Time series analysis by state space method. Oxford: Oxford University Press.
13. Enns PG, Machak JA, Spivey A and Wrobleski WJ (1982) Forecasting applications of an adaptive multiple exponential smoothing model. Management Science, 28, 1035-44.
14. Gardner ES and McKenzie E (1985) Forecasting trends in time series. Management Science, 31, 1237-46.
15. 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.
16. Granger CW and Newbold P (1986) Forecasting economic time series, 2nd edn.New York: Academic Press.
17. Hamilton JD (1994) Time series analysis. Princeton: Princeton University Press.
18. Hannan EJ (1969) The identification of vector mixed Autoregressive-Moving Average systems. Biometrica56, 223-25.
19. Hannan EJ and Deistler M (1988) The statistical theory of linear systems. New York: John Wiley & Sons.
20. Harvey A (1986) Analysis and generalisation of a multivariate exponential smoothing model. Management Science, 32, 374-80.
21. 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.
22. Harvey AC (1989) Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press.
23. Hyndman R and Koehler AB (2006) Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679-88.
24. Hyndman RJ (2001) It's time to move from 'what' to 'why'. International Journal of Forecasting, 17, 567-70.
25. Hyndman RJ, Koehler AB, Ord JK and Snyder RD (2008) Forecasting with exponential smoothing: the state space approach. Berlin: Springer.
26. 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.
27. Jones RH (1966) Exponential smoothing for multivariate time series. Journal of the Royal Statistical Society, Series B, 28, 241-51.
28. 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.
29. Lütkepohl H (2005) New introduction to multiple time series analysis. Berlin: Springer-Verlag.
30. Makridakis M and Hibon M (2000) The M3-competition: results, conclusions and implications. International Journal of Forecasting, 16, 451-76.
31. Meese R and Rognoff K (1983) Empirical exchange rate model of the seventies. Journal of International Economics, 14, 3-24.
32. 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.
33. Pfeffermann D and Allon J (1989) Multivariate exponential smoothing: method and practice. International Journal of Forecasting, 5, 83-98.
34. Sims C (1980) Macroeconomics and reality. Econometrica, 48, 1-49.
35. Snyder RD (1985) Recursive estimation of dynamic linear statistical models. Journal of the Royal Statistical Society, Series B, 47, 272-76.
36. Tiao G and Tsay R (1989) Model specification in multivariate time series. Journal of the Royal Statistical Society, Series B, 51, 157-213.
37. 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.