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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

本研究的主要内容如下：1.使用信息准则EIC：使用扩展信息准则EIC的季节调整进行了讨论。结果表明，该方案在样本外预测中具有选择趋势的自然倾向.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。揭示了在对各序列分别进行季节性调整和趋势去除时，存在丢失相关序列间相互关系信息的危险. X-11型模型：我们尝试用基于现代模型的季节调整方法重建X-11型趋势估计。我们介绍了一种偏差校正方法，该方法将与传统的基于加性模型的趋势估计一起使用。我们还介绍了一种新的模式，它具有X-11型的趋势，为乘法， ...更多信息 ve系列。利用新的模型，我们可以直接从数据中估计X-11类型的趋势.蒙特卡罗滤波：提出了一种用于高维非线性非高斯状态空间模型状态估计的蒙特卡罗滤波和平滑方法。在此基础上，考虑了季节调整的各种模型，如：G.（2）异常值的处理（3）乘性模型的估计（4）超参数的贝叶斯估计。遗传算法：研究了遗传算法与蒙特卡洛滤波器的关系。本文的主要目的是通过从蒙特卡罗滤波器的角度对遗传算法进行解释，将遗传算法转换为贝叶斯框架.对DECOMP算法的改进：提出了一种提取平稳自回归分量的方法。其中考虑了频域中带约束数值优化问题。然而，进一步的研究仍然没有完成。特别是在造型跳跃和扭结的趋势.协整模型：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方法需要两个步骤. X-12-REGARIMA：提高其预测能力。基于AIC的回归模型选择程序被纳入传统的基于移动平均的X-11季节调整程序。少