Estimation and prediction for a class of dynamic nonlinear statistical models

被引：141
|
作者：
Ord, JK [1 ]
Koehler, AB
Snyder, RD
机构：
[1] Penn State Univ, Dept Management Sci & Informat Syst, University Pk, PA 16802 USA
[2] Penn State Univ, Dept Stat, University Pk, PA 16802 USA
[3] Miami Univ, Dept Decis Sci & Management Informat Syst, Oxford, OH 45056 USA
[4] Monash Univ, Dept Econometr, Clayton, Vic 3168, Australia
来源：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 1997年 / 92卷 / 440期
关键词：
forecasting; Holt-Winters method; maximum likelihood estimation; state-space models;
D O I：
10.2307/2965433
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：1621 / 1629
页数：9
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