Statistical Methods for the Seasonal Adjustment

季节调整的统计方法

基本信息

批准号：
04045056
负责人：
ISHIGURO Makio
金额：
$ 3.52万
依托单位：
The Institute of Statistical Mathematics
依托单位国家：
日本
项目类别：
Grant-in-Aid for international Scientific Research
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1994
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-04045056/
关键词：
EIC Multivariate time series analysis Dynamic model X-11 Monte Carlo filter Genetic Algoritm Co-integration AIC 季節調整経済システムモンテカルロ法状態定門モデル情報量規準平滑化法統計的モデル BAYSEA ベイズモデルトレンド解析

项目摘要

Topics covered in this study are as follows :1. Use of Information Criterion EIC : Use of Extended Information Criterion EIC for the seasonal adjustment is discussed. It is demonstrated that the proposed resampling scheme shows a a natural tendency for chosing better trend for the out-of-sample forcasting.2. Multivariate Economic Series Analysis : This study intended to analyze the role of sesonal adjustment procedure as a preprocessing technique for the multivariate timeseries analysis. It is reveald that there is danger of losing information about the mutual relationship among related series, when each series is adjusted for the seasonality and detrended separately.3. X-11 type model : We tried to reconstruct the X-11 type trend estimate by the modern model-based seasonal adjustment method. We introduced a bias correction method which is to be used with conventional additive type model-based trend estimate. We also introduced a new model which has the X-11 type trend for multiplicati … More ve series. With the new model we can estimate the X-11 type trend directly from the data.4. Monte Carlo filtering : A Monte Carlo filtering and smoothing methods have been developed for state estimation of high-dimensional nonlinear non-Gaussian state space models. Based on this methods, various models for seasonal adjustment are considered, e. g. , (1) detection of jumps of trend or seasonal components (2) treatment of outliers (3) estimation of multiplicative model (4) Baysian estimation of hyper-parametrs.5. Genetic Algoritm : We investigate the relationships between the Genetic Algoritm and Monte Calro Filter. The major objective of this paper is to cast the Genetic Algorithm into the Baysian framework by its interpretation from a viewpoint of the Monte Carlo filter.6. Improve of DECOMP : A procedure for extracting 'stable' stationary autoregressive component is proposed. in which we consider a numerical optimization with a restriction in frequency domain. Further research remained undone, however. especially in modeling jump and kink in trend.7. Co-integration model : State-space representation for co-integration model is proposed which enables us to estimate the unknown parameters in one-step. while traditional Engle-Granger's method needs two steps.8. X-12-REGARIMA : To improve its forcasting ability. AIC based regression model selection procedure is incorporated in the traditional moving-avarage based X-11 seasonal adjustment program. Less

本研究涵盖的主题如下： 1. 信息准则 EIC 的使用：讨论了使用扩展信息准则 EIC 进行季节调整事实证明，所提出的重采样方案显示出选择更好趋势的自然趋势。样本预测.2. 多元经济序列分析：本研究旨在分析季节调整程序作为多元时间序列分析的预处理技术的作用。当每个序列进行季节性调整并分别去除趋势时，会丢失相关序列之间相互关系的信息：我们尝试通过基于现代模型的季节调整来重建X-11型趋势估计。3．我们引入了一种与传统的基于加法类型模型的趋势估计一起使用的偏差校正方法，我们还引入了一种新模型，该模型具有用于乘数的 X-11 类型趋势。直接估计X-11型趋势4. 蒙特卡罗滤波：已经开发了一种用于高维非线性非高斯状态空间模型的状态估计的蒙特卡罗滤波和平滑方法，基于该方法，考虑了各种季节调整模型。（1）趋势或季节性成分跳跃的检测（2）异常值的处理（3）乘法模型的估计（4）超参数的贝叶斯估计。5。遗传算法和蒙特卡罗滤波器之间的关系本文的主要目的是从蒙特卡罗滤波器的角度将遗传算法引入贝叶斯框架。 6． ' 提出了静态自回归组件，其中我们考虑了频域限制的数值优化，但是，特别是在趋势协整模型中的建模。：提出了协整模型的状态空间表示，使我们能够一步估计未知参数，而传统的Engle-Granger方法需要两步。8．X-12-REGARIMA：提高其预测能力。基于回归模型选择程序被纳入传统的基于移动平均的 X-11 季节性调整程序。