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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
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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