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Economic Time Series and Seasonal Adjustment Methods

经济时间序列和季节调整方法

基本信息

批准号：
09630024
负责人：
KUNITOMO Naoto
金额：
$ 1.54万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

项目摘要

The main purpose of this project was to re-examine the existing statistical methods often used in making the published economic time series data from the central and local governments in Japan. In particular we have investigated the X-12-ARIMA method recently developed by the U.S.Census office and the DECOMP method developed by Professor Kitagawa of the Institute of Statistical Mathematics.First we have inverstigated the major improvements in the X-12-ARIMA method, which is a revised version of the Census X-11 method. Since the X-11 method has been commonly used among Japanese governrment officials, the meaning of improvements have been the central issues in our study. We found that we can often get stable time series data sets by using the X-12-ARIMA methods, but also found that it really depends on the selection of the seasonal ARIMA models used in the program. Another issue has been whether we should use the trading day adjustments and the Leap year adjustments in order to make the … More official time series. Since the time seresi cycles behind the trading day effects and the Leap year effects are not seasonal (i.e. 12 months cycles), it has been still controversial if we use these options in the X-12-ARIMA program. We also have investigated the spectral properties of the residuals from the X-12-ARIMA program and the DECOMP program. We found that the estimated spectrum from the X-12-ARIiMA residuals often are smooth while the estimated spectrum from the DECOMP residuals have sometimes dips in the seasonal cycles. We have tried to investigated if this phenomenon is the result of the optimal properties of the DECOMP program in the sense of MSE.This problem was pointed out by the classical study on the seasonal adjustment methods by Grether and Nerlove and we have done some Simulation studies. However, we could not have reach a firm conclusion on this issue. Given our investigations, we have an impression that we need more study on these two seasonal adjustment programs from the theoretical side as well as the practical side in the Japanese governments.In conclusion, we have acomplished the most important objectives of this project. Three members participated in this project has written some papers and also stimulated a large number of researchers in the related fields and some statisticians in the Japanese governments We thank The Ministry of Education, Science and Culture for giving the generous support to research project. Less

该项目的主要目的是重新审查日本中央和地方政府发布的经济时间序列数据中常用的现有统计方法。我们特别研究了美国人口普查办公室最近开发的 X-12-ARIMA 方法和统计数学研究所北川教授开发的 DECOMP 方法。首先，我们研究了 X-12-ARIMA 方法的主要改进，该方法是人口普查 X-11 方法的修订版本。由于X-11方法已在日本政府官员中普遍使用，改进的意义一直是我们研究的中心问题。我们发现使用 X-12-ARIMA 方法通常可以获得稳定的时间序列数据集，但也发现它实际上取决于程序中使用的季节性 ARIMA 模型的选择。另一个问题是我们是否应该使用交易日调整和闰年调整来制作官方时间序列。由于交易日效应和闰年效应背后的时间塞雷西周期不是季节性的（即12个月周期），因此在X-12-ARIMA程序中是否使用这些选项仍然存在争议。我们还研究了 X-12-ARIMA 程序和 DECOMP 程序残差的光谱特性。我们发现 X-12-ARIiMA 残差的估计频谱通常是平滑的，而 DECOMP 残差的估计频谱有时会在季节周期中下降。我们试图研究这种现象是否是MSE意义上的DECOMP程序最优性质的结果。这个问题是Grether和Nerlove对季节调整方法的经典研究指出的，我们也做了一些模拟研究。然而，我们无法就这个问题得出明确的结论。根据我们的调查，我们的印象是，日本政府需要从理论和实践方面对这两个季节调整计划进行更多的研究。总之，我们已经完成了该项目最重要的目标。参与该项目的三位成员撰写了一些论文，也激励了一大批相关领域的研究人员和日本政府的一些统计人员。感谢文部科学省对研究项目的大力支持。较少的