RECURSIVE FORECASTING, SMOOTHING AND SEASONAL ADJUSTMENT OF NONSTATIONARY ENVIRONMENTAL DATA

被引：66
|
作者：
YOUNG, PC
NG, CN
LANE, K
PARKER, D
机构：
[1] CITY POLYTECH, HONG KONG, HONG KONG
[2] METEOROL OFF, BRACKNELL RB12 2SZ, BERKS, ENGLAND
来源：
JOURNAL OF FORECASTING | 1991年 / 10卷 / 1-2期
关键词：
NON-STATIONARITY; COMPONENT MODEL; STATE SPACE RECURSIVE ESTIMATION; FORECASTING AND SMOOTHING ADAPTIVE SEASONAL ADJUSTMENT ATMOSPHERIC CO2;
D O I：
10.1002/for.3980100105
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
The paper presents a unified, fully recursive approach to the modelling, forecasting and seasonal adjustment of non‐stationary time series and shows how it can be used as a flexible tool in the analysis of environmental data. The approach is based on time‐variable parameter (TVP) versions of various well‐known time‐series models and exploits the suite of novel, recursive filtering and fixed interval smoothing algorithms available in the microCAPTAIN computer program. The practical utility of the analysis is demonstrated by an example based on the analysis of atmospheric CO2 and sea surface temperature anomaly data. Copyright © 1991 John Wiley & Sons, Ltd.
引用
收藏
页码：57 / 89
页数：33
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