Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/oxford-university-press/partial-non-gaussian-state-space-p3y3VTquHg
References (32)
-
B. Carlin, Nicholas Polson, D. Stoffer (1992)
A Monte Carlo Approach to Nonnormal and Nonlinear State-Space ModelingJournal of the American Statistical Association, 87
-
Denise Osborn, Andrew Harvey (1991)
Forecasting, Structural Time Series Models and the Kalman Filter.Economica, 58
-
L. Tierney (1994)
Markov Chains for Exploring Posterior DistributionsAnnals of Statistics, 22
-
S. Koopman, N. Shephard (1992)
Exact score for time series models in state space formBiometrika, 79
-
J. Hull, Alan White (1987)
Hedging the Risks from Writing Foreign Currency OptionsJournal of International Money and Finance, 6
-
A. Harvey, E. Ruiz, N. Shephard (1994)
Multivariate stochastic variance models -
Richard Smith, J. Miller (1986)
A non-Gaussian state space model and application to prediction of recordsJournal of the royal statistical society series b-methodological, 48
-
A. Dempster, N. Laird, D. Rubin (1977)
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper -
T. Bresnahan (1981)
Departures from marginal-cost pricing in the American automobile industry: Estimates for 1977–1978Journal of Econometrics, 17
-
R. Fildes (1992)
Forecasting structural time series models and the kalman filter: Andrew Harvey, 1989, (Cambridge University Press), 554 pp., ISBN 0-521-32196-4International Journal of Forecasting, 8
-
S. Frühwirth-Schnatter (1994)
Data Augmentation and Dynamic Linear ModelsJournal of Time Series Analysis, 15
-
W. Qian, D. Titterington (1991)
Estimation of parameters in hidden Markov modelsPhilosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences, 337
-
N. Shephard (1994)
Local scale models: State space alternative to integrated GARCH processesJournal of Econometrics, 60
-
A. Rukhin (1991)
Tools for statistical inference -
P. Jong (1989)
Smoothing and Interpolation with the State-Space ModelJournal of the American Statistical Association, 84
-
D. Alspach, H. Sorenson (1972)
Nonlinear Bayesian estimation using Gaussian sum approximationsIEEE Transactions on Automatic Control, 17
-
Angelo Melino, S. Turnbull (1990)
Pricing foreign currency options with stochastic volatilityJournal of Econometrics, 45
-
Adrian Smith, G. Roberts (1993)
Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus -
B. Ripley (1987)
Stochastic simulation -
B. Anderson, J. Moore, M. Eslami (1979)
Optimal FilteringIEEE Transactions on Systems, Man, and Cybernetics, 12
-
N. Shephard (1993)
Fitting Nonlinear Time-Series Models with Applications to Stochastic Variance ModelsJournal of Applied Econometrics, 8
-
Chris Carter, R. Kohn (1994)
On Gibbs sampling for state space modelsBiometrika, 81
-
James Hamilton (1989)
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business CycleEconometrica, 57
-
(1988)
Bayesian approach to robustifying the Kalman filter -
A. Dempster, N. Laird, D. Rubin (1972)
Max-imum Likelihood from Incomplete DataElearn
-
G. Kitagawa (1987)
Non-Gaussian state space modeling of time series26th IEEE Conference on Decision and Control, 26
-
M. Chesney, Louis Scott (1989)
Pricing European Currency Options: A Comparison of the Modified Black-Scholes Model and a Random Variance ModelJournal of Financial and Quantitative Analysis, 24
-
(1985)
The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem -
M. West, J. Harrison (1989)
Bayesian forecasting and dynamic models -
S. Koopman (1993)
Disturbance smoother for state space modelsBiometrika, 80
-
G. Wei, M. Tanner (1990)
A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation AlgorithmsJournal of the American Statistical Association, 85
-
G. Kitagawa (1989)
Non-Gaussian seasonal adjustmentComputers & Mathematics With Applications, 18
- Publisher
- Oxford University Press
- Copyright
- © 1994 Biometrika Trust
- ISSN
- 0006-3444
- eISSN
- 1464-3510
- DOI
- 10.1093/biomet/81.1.115
- Publisher site
- See Article on Publisher Site
Abstract
SUMMARY In this paper we suggest the use of simulation techniques to extend the applicability of the usual Gaussian state space filtering and smoothing techniques to a class of non-Gaussian time series models. This allows a fully Bayesian or maximum likelihood analysis of some interesting models, including outlier models, discrete Markov chain components, multiplicative models and stochastic variance models. Finally we discuss at some length the use of a non-Gaussian model to seasonally adjust the published money supply figures. This content is only available as a PDF. © 1994 Biometrika Trust
Journal
Biometrika – Oxford University Press
Published: Mar 1, 1994