Nonregular Time Series Analysis and Econometric Methods
Project/Area Number  06630017 
Research Category 
GrantinAid for Scientific Research (C).

Research Field 
Economic statistics

Research Institution  Faculty of Economics, University of Tokyo 
Principal Investigator 
国友 直人 東京大学, 経済学部, 教授
KUNITOMO Naoto Faculty of Economics, University of Tokyo, Professor, 経済学部, 教授 (10153313)

CoInvestigator(Kenkyūbuntansha) 
YAJIMA Yoshihiro Faculty of Economics, University of Tokyo, Associate Professor, 経済学部, 助教授 (70134814)

Project Fiscal Year 
1994 – 1995

Project Status 
Completed(Fiscal Year 1995)

Budget Amount *help 
¥1,500,000 (Direct Cost : ¥1,500,000)
Fiscal Year 1995 : ¥600,000 (Direct Cost : ¥600,000)
Fiscal Year 1994 : ¥900,000 (Direct Cost : ¥900,000)

Keywords  Time Series Analysis / Nonlinearity / Unit roots / Cointegration / Strong dependence / Simulataneous Switching / Missing Observation / Financial Time Series / 時系列解析 / 計量経済分析 / 強従属性 / 非定常性 / 単位根検定 / 共和分関係 / 欠足時系列 / 同時転換時系列 / 非線形モデル / 転換時系列モデル / 非正則スペクトル密度関数 
Research Abstract 
The main purpose of this project was to reexamine 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 nonlinearity and nonstationarity in economic phenomena. One important aspect of nonlinearity in many economic time series and financial time series is the asymmetrical movements of time series in the upward phase and the downword phase. Since it is not possible to describe this aspect by the stationary linear autoregressive movingaverage (ARMA) model or the linear autoregressive integrated movingaverage (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
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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 nonstationary 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 longmemory property in economic time series. One important aspect of the longmemory 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 nonstationarities in economic time series. One important aspect to nonstationarity 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 prespecified 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

Report
(4results)
Research Output
(27results)