Structural Time Series Models and the Kalman Filter: A Concise Review
Structural Time Series Models and the Kalman Filter: A Concise Review
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结构时间序列模型和卡尔曼滤波器：简要回顾
DOI：
10.2139/ssrn.1496864
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发表时间：
2009
影响因子：
2
通讯作者：
J. Jalles
中科院分区：
文献类型：
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作者：
J. Jalles
近年来，经济数据的可用性持续增加，更重要的是，构建更大频率时间序列的可能性，促进了统计和计量经济技术的使用（和发展）以更准确地处理它们。本文阐述了结构时间序列模型，通过该模型可以将时间序列分解为趋势、季节性和不规则分量的总和。除了单变量规范的详细分析之外，我们还解决了 SUTSE 多变量情况和协整问题。最后，考虑到从初始化到参数估计的不同阶段，描述了通过卡尔曼滤波器算法进行递归估计和平滑。 JEL 代码：C10、C22、C32
The continued increase in availability of economic data in recent years and, more impor- tantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci?cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman ?lter algorithm is described taking into account its di¤erent stages, from initialisation to parameter?s estimation. JEL codes: C10, C22, C32
DOI：
10.2307/2528652
发表时间：
1972-07
期刊：
--
影响因子：
--
作者：
T. Anderson
通讯作者：
T. Anderson