Decomposition of time series models in state-space form

Computational Statistics and Data Analysis

Volumn 50, Issue 9, 2006, Pages 2232-2246

  Godolphin, E J ^a   Triantafyllopoulos, Kostas ^b  

^a UNIVERSITY OF LONDON   (United Kingdom)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Bayesian forecasting; Decompositions of time series; Dynamic models; Generalized linear models; Kalman filtering; State space models

Indexed keywords

DATA REDUCTION; KALMAN FILTERING; LINEAR EQUATIONS; MATHEMATICAL MODELS; STATE SPACE METHODS;

BAYESIAN FORECASTING; DECOMPOSITIONS OF TIME SERIES; DYNAMIC MODELS; GENERALIZED LINEAR MODELS;

TIME SERIES ANALYSIS;

EID: 33644654454     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2004.12.012     Document Type: Article

References (27)

1. Davis, H.T., 1941. The Analysis of Economic Time Series, Cowles Commission Monograph No. 6. Principia Press, Bloomington, Indiana.
2. J. Durbin, and S.J. Koopman Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives (with discussion) J. Roy. Statist. Soc. B 62 1 2000 3 56
3. J. Durbin, and S.J. Koopman Time Series Analysis by State Space Methods 2001 Oxford University Press Oxford
4. L. Fahrmeir, and G. Tutz Multivariate Statistical Modelling Based on Generalized Linear Models 1994 Springer New York
5. D. Gamerman Dynamic Bayesian models for survival data Appl. Statist. 40 1 1991 63 79
6. M. Goldstein Bayesian analysis of regression problems Biometrika 63 1 1976 51 58
7. E.J. Godolphin Observable trend-projecting state-space models J. Appl. Statist. 28 3/4 2001 379 389
8. E.J. Godolphin, and S.E. Johnson Decomposition of time series dynamic linear models J. Time Ser. Anal. 24 5 2003 513 527
9. E.J. Godolphin, and J.M. Stone On the structural representation for polynomial-projecting models based on the Kalman filter J. Roy. Statist. Soc. B 42 1 1980 35 45
10. P.J. Harrison, and C.F. Stevens Bayesian forecasting (with discussion) J. Roy. Statist. Soc. B 38 3 1976 205 247
11. A.C. Harvey, and C. Fernandes Time series models for count or qualitative observations J. Bus. Econom. Statist. 7 4 1989 407 417
12. K. Hemming, and J.E.H. Shaw A parametric dynamic survival model applied to breast cancer survival times Appl. Statist. 51 4 2002 421 435
13. S.C. Hillmer, and G.C. Tiao ARIMA-model based approach to seasonal adjustment J. Amer. Statist. Assoc. 77 377 1982 63 70
14. P.B. Key, and E.J. Godolphin On the Bayesian steady forecasting model J. Roy. Statist. Soc. B 43 1 1981 92 96
15. G. Kitagawa, and W. Gersch Smoothness Priors Analysis of Time Series 1996 Springer New York
16. P. McCullagh, and J.A. Nelder Generalized Linear Models second ed 1989 Chapman & Hall London
17. W.C. Mitchell Business Cycles, the Problem and Setting 1927 The National Bureau of Economic Research New York
18. D.S.G. Pollock Trend estimation and de-trending via rational squarewave filters J. Econometrics 99 2 2000 317 334
19. D.S.G. Pollock Methodology for trend estimation Econom. Model. 18 1 2001 75 96
20. D.S.G. Pollock Recursive estimation in econometrics Comput. Statist. Data Anal. 44 1/2 2003 37 75
21. J.Q. Smith A generalization of the Bayesian steady forecasting model J. Roy. Statist. Soc. B 41 3 1979 375 387
22. R.L. Smith, and J.E. Miller A non-Gaussian state space model and application to prediction of records J. Roy. Statist. Soc. B 48 1 1986 79 88
23. K. Triantafyllopoulos, and J. Pikoulas Multivariate Bayesian regression applied to the problem of network security J. Forecasting 21 8 2002 579 594
24. M. West Time series decompositions Biometrika 84 2 1997 489 494
25. M. West, and P.J. Harrison Bayesian Forecasting and Dynamic Models second ed 1997 Springer New York
26. P. Whittle Prediction and Regulation by Linear Least Square Methods 1963 English University Press London
27. G.U. Yule On a method of investigating periodicities in disturbed series with special reference to Wolfer's sunspot numbers Philos. Trans. Roy. Soc. 226 1927 267 298