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，特别是我们研究了日本中央政府最近开发的 X-12-ARIMA 方法。美国人口普查局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，它是人口普查的修订版X-11方法。由于X-11方法已在日本政府官员中普遍使用，因此改进的意义一直是我们研究的中心问题，我们发现使用X-11方法经常可以获得稳定的时间序列数据集。 12-ARIMA 方法，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使… 更多时间系列以来。交易日效应和闰年效应背后的塞雷西周期不是季节性的（即 12 个月周期），如果我们在 X-12-ARIMA 程序中使用这些选项，仍然存在争议。我们还研究了光谱特性。 X-12-ARIMA 程序和 DECOMP 程序的残差我们发现 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是平滑的。我们试图研究这种现象是否是 MSE 意义上的 DECOMP 程序的最优性质的结果。Grether 和 Nerlove 对季节调整方法的经典研究指出了这个问题。我们已经做了一些模拟研究，但是，根据我们的调查，我们无法得出明确的结论，我们需要从理论和实践方面对这两个季节调整方案进行更多研究。在日本政府中。总而言之，我们有完成了这个项目最重要的目标。参与这个项目的三名成员写了一些论文，也刺激了大批相关领域的研究人员和日本政府的一些统计学家。我们感谢文部科学省的给予。对研究项目的慷慨支持较少。