| 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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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 2005: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2004: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2003: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Multivariate Economic Time Series / Count Data / Markov chain Monte Carlo / Sore Level Reference Price / Spatial Time Series / Co-Gaussianity / マルコフ連鎖モンテカルロ法 / 結合予測 / 多変量ガウス性検定 / 多変量時系列 / 非線形性検定 / ブートストラップ / サロゲートデータ / 有限標本特性 |
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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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