2005 Fiscal Year Final Research Report Summary
Statistical modeling of economic time series based on the tests of multivariate Gaussianity and linearity
| Project/Area Number |
15530137
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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 |
TERUI Nobuhiko Tohoku University, Graduate School of Economics and Management, Professor, 大学院・経済学研究科, 教授 (50207495)
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| Project Period (FY) |
2003 – 2005
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| Keywords | Multivariate Economic Time Series / Count Data / Markov chain Monte Carlo / Sore Level Reference Price / Spatial Time Series / Co-Gaussianity |
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| Research Abstract |
Based on a characterization of orthogonality of Gaussian random variates after Hermitian polynomials transformation, we develop a Gaussianity test for multivariate stationary time series, where amultivariate Gaussianity test proposed by Terui and Imano(2003), I conducted the research on the statistical modeling of economic time series based on the tests of multivariate Gaussianity and linearity as follows.
1.The improvement of speed of convergence of test statistics for multivariate Gaussinaity was conducted by using Bootstrap.
2.I conducted the research that generalizes the combined forecasts between linear nad some nonlinear time series forecasts by Terui and van Dijk(2002) for univariate time series to multivariate series.
3.As a statistical modeling of non-Gaussian time series, I did the research of time series models for count data, such as multinomial and poison variables. The dynamic Bayesian linear modeling by using state space representation and their MCMC algorithm was investigated. I showed that this modeling could be useful for sale forecasting of number of sales of product, where the market expansion and shrink can be incorporated in the model.
4.I gave a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable - price promotion - by threshold models. As a threshold variable to generate a mechanism for different market responses, we use the counterpart to the concept of a price threshold applied to a representative consumer in a store. A Bayesian approach is taken for statistical modeling because of advantages that it offers over estimation and forecasting. The proposed model incorporates the lagged effects of a price variable. Thereby, myriad pricing strategies can be implemented in the time horizon. Their effectiveness can be evaluated using the predictive density. We intend to improve the forecasting performance over conventional linear time series models.
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[Book] ベイズ計量経済分析2005
Author(s)
和合肇編著(照井伸彦を含む13名)
Total Pages
387
Publisher
東洋経済新報社
Description
「研究成果報告書概要(和文)」より
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