Time series with additive noise

Biometrika

Volumn 86, Issue 2, 1999, Pages 474-482

  So, Mike K P ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Autoregressive fractionally integrated moving average; Kalman filter; Long memory; Long range dependence; Markov chain Monte Carlo; Outlier detection; State space model; Stochastic volatility model

Indexed keywords

EID: 0442320580     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/86.2.474     Document Type: Article

References (21)

1. BARNETT, G., KOHN, R. & SHEATHER, S. (1996). Bayesian estimation of an autoregressive model using Markov chain Monte Carlo. J. Economet. 14, 237-54.
2. BARNETT, G., KOHN, R. & SHEATHER, S. (1997). Robust Bayesian estimation of autoregressive-moving-average models. J. Time Ser. Anal. 18, 11-28.
3. BERAN, J. (1994). Statistics for Long-Memory Processes. London: Chapman and Hall.
4. BOX, G. E. P. & JENKINS, G. M. (1976). Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day.
5. BREIDT, F. J., CARTO, N. & DE LIMA, P. (1998). On the detection and estimation of long memory in stochastic volatility. J. Economet. 83, 325-48.
6. CARTER, C. K. & KOHN, R. (1994). On Gibbs sampling for state space models. Biometrika 81, 541-53.
7. CARTER, C. K. & KOHN, R. (1996). Markov chain Monte Carlo in conditionally Gaussian state space models. Biometrika 83, 589-601.
8. CHAN, N. H. & PALMA, W. (1998). State space modeling of long-memory processes. Ann. Statist. 26, 719-40.
9. DE JONG, P. & SHEPHARD, N. (1995). The simulation smoother for time series models. Biometrika 82, 339-50.
10. DURBIN, J. & KOOPMAN, S. J. (1997). Monte Carlo maximum likelihood estimation for non-Gaussian state space models. Biometrika 84, 669-84.
11. FRUHWIRTH-SCHNATTER, S. (1994). Data augmentation and dynamic linear models. J. Time Ser. Anal. 15, 183-202.
12. GRANGER, C. W. J. & JOYEUX, R. (1980). An introduction to long-memory time series models and fractional differencing. J. Time Ser. Anal. 1, 15-30.
13. HARVEY, A. C. (1989). Forecasting Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
14. HARVEY, A. C. (1993). Time Series Models. New York: Harvester Wheatsheaf.
15. HOSKING, J. R. M. (1981). Fractional differencing. Biometrika 68, 165-76.
16. KIM, S., SHEPHARD, N. & CHIB, S. (1998). Stochastic volatility: likelihood inference and comparison with ARCH models. Rev. Econ. Studies 65, 361-93.
17. KOOPMAN, S. J. (1993). Disturbance smoother for state space models. Biometrika 80, 117-26.
18. MCCULLOCH, R. E. & TSAY, R. S. (1994). Bayesian analysis of autoregressive time series via the Gibbs sampler. J. Time Ser. Anal. 15, 235-50.
19. SHEPHARD, N. (1994). Partial non-Gaussian state space. Biometrika 81, 115-31.
20. SHEPHARD, N. & PITT, M. K. (1997). Likelihood analysis of non-Gaussian measurement time series. Biometrika 84, 653-67.
21. WEST, M. & HARRISON, P. J. (1997). Bayesian Forecasting and Dynamic Models. New York: Springer-Verlag.