1998 Fiscal Year Final Research Report Summary
Causal Structure Analysis of Economic Time-Series Data : Method and Application
| Project/Area Number |
09630023
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Economic statistics
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| Research Institution | Tohoku University |
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Principal Investigator |
HOSOYA Yuzo Tohoku University, Faculty of Economics, Professor, 経済学部, 教授 (40004197)
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| Project Period (FY) |
1997 – 1998
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| Keywords | econometrics / causal test / causal measure / Wald test / cointegration model |
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| Research Abstract |
The results of this research period consist of the theoretical development of statistical inference method for the causal measure between economic time-series and its econometric application.
The researcher has already given a theory of causal measure for stationary time-series. The present research, in particular, is focused on the one-way effect measure and gives the Wald test and the confidence set construction method based on the Johansen nonstationary co-integration model, developing also the accompanying computer algorithms. The method is applied to the quarterly data set of Japanese macroeconomy, namely the GDP, the call rates, exports and imports over the recent 20 years. By testing and evaluating the one-way causal strength of various combinations of those time-seires, the Japanese macroeconomic structure is characterized in terms of mutual causal interaction. A merit of the inferential method of this research is that it enables not only testing but estimating causal strength, providing the confidence region, whereas the conventional Granger tests merely test the presence of one-way effect. Specifically, the present approach enables constructing simultaneous confidence-regions of plural one-way effect measures by means of the Wald test. Another contribution of this research is the development of a new theory of eliminating the one-way effect of a third series in case of the presence and the development of an computer algorithms for it. The merit of the elimination method is in that it can evade the difficulties of the conventional methods by Granger and Geveke. Lastly for this purpose, the research developed an improved factorization algorithm of rational spectral density matrices.
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