2013 Fiscal Year Final Research Report
Non-linear transformation tim-series models and causal analysis
| Project/Area Number |
22530211
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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 | Meisei University |
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Principal Investigator |
HOSOYA Yuzo 明星大学, 経済学部, 教授 (40004197)
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| Project Period (FY) |
2010-04-01 – 2014-03-31
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| Keywords | 経済時系列 / 計量経済学 / 因果性測度 / 統計的推測 |
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| Research Abstract |
This research provides an approach to characterize the dependency structure between multivariate variables. Focused on the stationary ARMA model, the research produced a feasible way of numerically feasible method of conducting statistical estimation and testing of partial causal measures. Although focused on the stationary ARMA model, the approach has wide applicability in cointegrated time-series. The research conducted simulation study to evaluate the small-sample performance of the developed plug-in estimation method for the interdependence measures on the basis of large-scale Monte Carlo experiments and also the three-step maximum Whittle likelihood estimation procedure for the model parameters. The research also contributed in an empirical study on the U.S. macro and financial economy. In particular, intensive study was made on the causal structure among the term spread and growth rates of real GDP, M2 and CPI, adding a new insight to the literature of the allied field.
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Research Products
(9 results)
