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检查经常用于从日本中央和地方政府发表的经济时间序列数据中使用的现有统计方法。特别是我们调查了美国企业办公室最近开发的X-12-Arima方法，以及由统计数学研究所Kitagawa教授开发的分解方法。首先，我们对X-12-Arima方法的主要改进进行了census X-111方法的重新改进。由于X-11方法已在日本政府官员中使用，因此改进的含义一直是我们研究中的核心问题。我们发现，我们通常可以使用X-12-Arima方法获得稳定的时间序列数据集，但也发现它确实取决于该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leap年调整来制作……更正式的时间序列。由于交易日效应和leap年效应的时间塞雷西周期的时间不是季节性的（即12个月的周期），因此，如果我们在X-12-Arima计划中使用这些选项，那仍然是有争议的。我们还研究了X-12-Arima计划和分区程序的残差的光谱特性。我们发现，来自X-12-Ariima残差的估计光谱通常是光滑的，而分解残差的估计光谱有时会在季节性周期中下降。我们试图研究这种现象是否是MSE的最佳特性的结果。但是，我们无法就此问题得出确定的结论。考虑到我们的调查，我们的印象是，我们需要对日本政府的这两个季节性调整计划以及日本政府的实际方面进行更多研究。总而言之，我们提供了该项目最重要的目标。参与该项目的三名成员已经撰写了一些论文，还激发了相关领域的大量研究人员和日本政府的一些统计学家，我们感谢教育，科学和文化部为研究项目提供慷慨的支持。较少的