You are not currently logged in.
Access JSTOR through your library or other institution:
Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models
J. K. Ord, A. B. Koehler and R. D. Snyder
Journal of the American Statistical Association
Vol. 92, No. 440 (Dec., 1997), pp. 1621-1629
Stable URL: http://www.jstor.org/stable/2965433
Page Count: 9
You can always find the topics here!Topics: Statistical models, Time series forecasting, Forecasting models, Statistical forecasts, Time series, Forecasting techniques, Musical intervals, Hems, Simulations, Time series models
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Preview not available
A class of nonlinear state-space models, characterized by a single source of randomness, is introduced. A special case, the model underpinning the multiplicative Holt-Winters method of forecasting, is identified. Maximum likelihood estimation based on exponential smoothing instead of a Kalman filter, and with the potential to be applied in contexts involving non-Gaussian disturbances, is considered. A method for computing prediction intervals is proposed and evaluated on both simulated and real data.
Journal of the American Statistical Association © 1997 American Statistical Association