Non-regular Time Series Analysis and Econometric Methods

非正则时间序列分析和计量经济学方法

基本信息

批准号：
06630017
负责人：
KUNITOMO Naoto
金额：
$ 0.96万
依托单位：
Faculty of Economics, University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

The main purpose of this project was to re-examine the existing statistical and econometric methods commonly used in analyzing economic time series data and develop some new time series methods. The other purpose of the project was to apply the methods we developed in this project to the economic time series data and financial time series data.There are many empirical evidences on the non-linearity and non-stationarity in economic phenomena. One important aspect of non-linearity in many economic time series and financial time series is the asymmetrical movements of time series in the up-ward phase and the down-word phase. Since it is not possible to describe this aspect by the stationary linear autoregressive moving-average (ARMA) model or the linear autoregressive integrated moving-average (ARIMA) model. N.Kunitomo has proposed the simultaneous switching autoregressive (SSAR) model with the collaboration of S.Sato (Institute of Statistical Mathematics) to describe the asymmetric movem … More ents in two different phases. Kunitomo=Sato (1994), and Sato=Kunitomo (1994) have investigated the various propeties of the stationary SSAR model and applied it to the analysis of some data in agricultural market. The SSAR model is closely related to some disequibrium models in econometrics. Then Kunitomo=Sato (1995) have extended the SSAR model and proposed the non-stationary SSAR (SSIAR) model. They have also applied it to the analysis of financial time series including Nikkei 225 spot and futures indeces.There are some empirical evidnece on the long-memory property in economic time series. One important aspect of the long-memory property can be characterized by the unboundedness of the spectal density of the stationary time series. Yajima (1995) have investigated this possibility and its theoretical outcomes.Also there are many empirical evidences on the non-stationarities in economic time series. One important aspect to non-stationarity in economic time series and financial time series is whether the linear integrated processes such as the autoregressive integrated moving average (ARIMA) model is appropriate or not in data analysis. This problem has been called the unit root testing problem. An important alternative possibility is the existence of structural changes in economic time series. Kunitomo (1995) and Kunitomo=Sato (1995) have investigated this possibility by allowing multiple change points and the number of change points could be unknown (but less than a pre-specified number.) Yajima=Nishino (1995) have investigated the unit root testing problem when some data are missing in economic time series.In conclusion, we have acomplished the most important objectives of this project. Two members participated in this project has written a large number of academic papers and also stimulated a large number of researchers in the related fields. We thank The Ministry of Education, Science and Culture for giving the generous support to our ambitious project. Less

该项目的主要目的是重新审视现有的常用于分析经济时间序列数据的统计和计量经济学方法，并开发一些新的时间序列方法，该项目的另一个目的是将我们在该项目中开发的方法应用于分析经济时间序列数据。许多经济现象的非线性和非平稳性的一个重要方面是经济时间序列和金融时间序列数据的不对称运动。上升阶段的时间序列由于无法通过平稳线性自回归移动平均（ARMA）模型或线性自回归积分移动平均（ARIMA）模型来描述这一方面，N.Kunitomo 提出了同时切换自回归。 (SSAR) 模型与 S.Sato（统计数学研究所）合作，描述两个不同阶段的不对称运动。 (1994)和Sato=Kunitomo(1994)研究了平稳SSAR模型的各种性质，并将其应用于农业市场的一些数据分析。SSAR模型与计量经济学中的一些不均衡模型密切相关。 Sato（1995）扩展了SSAR模型，提出了非平稳SSAR（SSIAR）模型，并将其应用于包括金融时间序列在内的分析。日经 225 现货和期货指数。关于经济时间序列的长期记忆特性，有一些经验证据可以通过平稳时间序列的谱密度的无界性来表征。 1995）研究了这种可能性及其理论结果。经济时间序列非平稳性的一个重要方面也有许多经验证据。金融时间序列的问题是诸如自回归综合移动平均（ARIMA）模型之类的线性综合过程在数据分析中是否合适，这个问题被称为单位根检验问题，一个重要的替代可能性是结构变化的存在。 Kunitomo (1995) 和 Kunitomo=Sato (1995) 通过允许多个变化点来研究这种可能性，并且变化点的数量可能是未知的（但小于预先指定的数量）。 Yajima=Nishino (1995) 研究了经济时间序列中某些数据缺失时的单位根检验问题。总之，我们完成了该项目最重要的目标，参与该项目的两名成员撰写了大量学术论文。我们感谢教育、科学和文化部对我们雄心勃勃的项目的慷慨支持。