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題名 兩群時間序列Granger領先關係之新檢定方法
New Tests of Granger Causality for two Groups of Time Series作者 張欣惠 貢獻者 洪英超
張欣惠關鍵詞 向量自我回歸模型
Granger 領先關係
Wald test
檢定力日期 2013 上傳時間 6-八月-2014 11:39:53 (UTC+8) 摘要 驗證兩群時間序列間之領先關係不但在經濟的領域上為重要的課題之一，在其他領域也被廣泛地應用。由於傳統檢定此多變量常態分配之平均向量的Uniformly most powerful(UMP) test 通常不存在，因此在本文中介紹一些新檢定統計量，用於檢定多變量Granger 領先關係檢定，判斷兩群時間序列間是否存在領先關係，並於定態(stationary) vector autoregression (VAR) 模型為背景下進行。這些新檢定統計量之接受域臨界值可以從多變量常態分配中計算或估計出，因此不論在操作或執行上皆相當容易，除此之外，在一些參數限制下，這些新檢定統計量皆有各自的使用時機，使得與傳統Wald test相比有較好之檢定力。最後，藉由美國兩組經濟指標資料進行實證分析，評估本研究建議之新檢定統計量。 參考文獻 Dekimpe, M. G., & Hanssens, D. M. (1995). The persistence of marketing effects on sales, Marketing Science, 14, 1-21.Dekimpe, M. G., & Hanssens, D. M. (1999). Sustained spending and persistent response : A new look at long-term marketing probability, Journal of Marketing research, 36, 397-412.Dufour, J. M., & Renault, E. (1998). Short-run and lonf-run causality in time series theory. Econometrica, 66, 1099-1125Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., & Hothorn, T. (2014). mvtnorm:Multivariate Normal and t Distributions. R package version 0.9-9997. Retrieved from http://CRAN.R-project.org/package=mvtnormGeweke, J., Meese, R., & Dent, W. (1983). Comparing alternative tests of causality in temporal systems. Journal of Econometrics, 21, 161-194.Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.Haugh, L. D. (1976). Checking the independence of two covariance-stationary time series: A univariate residual cross-correlation approach. Journal of the American Statistical Association, 71, 378-385.Hung, Y. C., & Tseng, N. F. (2012). Extracting informative variables in the validation of two-group causal relationship. Computational Statistics, 28(3), 1151-1167. Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin:Springer-Verlag.Murdoch, D., Chow, E. D.(2013). ellipse: Functions for drawing ellipses and ellipse-like confidence regions. R package version 0.3-8.Retrieved from http://CRAN.R-project.org/package=ellipsePierce, D. A., & Haugh, L. D. (1977). Causality in temporal systems: Characterization and a survey. Journal of econometrics, 5(3), 265-293.Ripley, B., Venables, B., Bates, D.M., Hornik, K., Gebhardt A, Firth, D. (2014). MASS: Support Functions and Datasets for Venables and Ripley`s MASS. R package version 7.3-33. Retrieved from http://CRAN.R-project.org/package=MASSSims, C. A. (1972). Money, income, and causality. The American Economic Review, 62(4), 540-552.Sims, C. A. (1980). Macroecomonics and reality. Journal of Econometrica, 48, 1-48.Srinivasan, S. and Bass, F.M (2001). Diagnosing competitive responsiveness: Disentangling retailer-induced and manufacturer induced actions. Paper presented at the MSI Conference on competitive responsiveness, Boston. Takada, H., & Bass, F. M. (1998). Multiple time series analysis of competitive marketing behavior. Journal of Business Research, 43(2), 97-107.Trapletti, A., Hornik, K., LeBaron, B. (2013). tseries: Time series analysis and computational finance. R package version 0.10-32. Retrieved from http://CRAN.R-project.org/package=tseriesTsai, M. T. M., & Sen, P. K. (1993). On the local optimality of optimal linear tests for restricted alternatives. Statistica Sinica, 3, 103-115. 描述 碩士
國立政治大學
統計研究所
101354012
102資料來源 http://thesis.lib.nccu.edu.tw/record/#G1013540121 資料類型 thesis dc.contributor.advisor 洪英超 zh_TW dc.contributor.author (作者) 張欣惠 zh_TW dc.creator (作者) 張欣惠 zh_TW dc.date (日期) 2013 en_US dc.date.accessioned 6-八月-2014 11:39:53 (UTC+8) - dc.date.available 6-八月-2014 11:39:53 (UTC+8) - dc.date.issued (上傳時間) 6-八月-2014 11:39:53 (UTC+8) - dc.identifier (其他 識別碼) G1013540121 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/68229 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計研究所 zh_TW dc.description (描述) 101354012 zh_TW dc.description (描述) 102 zh_TW dc.description.abstract (摘要) 驗證兩群時間序列間之領先關係不但在經濟的領域上為重要的課題之一，在其他領域也被廣泛地應用。由於傳統檢定此多變量常態分配之平均向量的Uniformly most powerful(UMP) test 通常不存在，因此在本文中介紹一些新檢定統計量，用於檢定多變量Granger 領先關係檢定，判斷兩群時間序列間是否存在領先關係，並於定態(stationary) vector autoregression (VAR) 模型為背景下進行。這些新檢定統計量之接受域臨界值可以從多變量常態分配中計算或估計出，因此不論在操作或執行上皆相當容易，除此之外，在一些參數限制下，這些新檢定統計量皆有各自的使用時機，使得與傳統Wald test相比有較好之檢定力。最後，藉由美國兩組經濟指標資料進行實證分析，評估本研究建議之新檢定統計量。 zh_TW dc.description.tableofcontents 第一章 導論 1第二章 Granger Non-causality 檢定 3第一節 向量自我回歸模型 3第二節 模型選擇與定態檢定 4第三節 多變量領先關係檢定 62.3.1 多變量領先關係 62.3.2 Wald檢定統計量 82.3.3 Wald檢定統計量之檢定力 9第四節 新檢定統計量 102.4.1 檢定統計量M 102.4.2 檢定統計量 M_s 112.4.3 檢定統計量 B 122.4.4 檢定統計量 B_s 13第三章 實際資料分析與模擬 14第一節 實例分析 143.1.1 定態檢定與模型選擇 143.1.2 領先關係檢定 15第二節 檢定統計量之拒絕域(接受域)與檢定力 163.2.2 統計量M之接受域 173.2.3 統計量 M_s 之接受域 173.2.4 統計量B 之接受域 183.2.5 統計量 B_s 之接受域 183.2.6 檢定統計量之接受域比較 19第三節 檢定力 20第四節 小樣本之接受域與檢定力 243.4.1 領先關係檢定 243.4.2 小樣本接受域與檢定力 25第五節 模擬研究 29參考文獻 36 zh_TW dc.format.extent 905273 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1013540121 en_US dc.subject (關鍵詞) 向量自我回歸模型 zh_TW dc.subject (關鍵詞) Granger 領先關係 zh_TW dc.subject (關鍵詞) Wald test zh_TW dc.subject (關鍵詞) 檢定力 zh_TW dc.title (題名) 兩群時間序列Granger領先關係之新檢定方法 zh_TW dc.title (題名) New Tests of Granger Causality for two Groups of Time Series en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) Dekimpe, M. G., & Hanssens, D. M. (1995). The persistence of marketing effects on sales, Marketing Science, 14, 1-21.Dekimpe, M. G., & Hanssens, D. M. (1999). Sustained spending and persistent response : A new look at long-term marketing probability, Journal of Marketing research, 36, 397-412.Dufour, J. M., & Renault, E. (1998). Short-run and lonf-run causality in time series theory. Econometrica, 66, 1099-1125Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., & Hothorn, T. (2014). mvtnorm:Multivariate Normal and t Distributions. R package version 0.9-9997. Retrieved from http://CRAN.R-project.org/package=mvtnormGeweke, J., Meese, R., & Dent, W. (1983). Comparing alternative tests of causality in temporal systems. Journal of Econometrics, 21, 161-194.Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.Haugh, L. D. (1976). Checking the independence of two covariance-stationary time series: A univariate residual cross-correlation approach. Journal of the American Statistical Association, 71, 378-385.Hung, Y. C., & Tseng, N. F. (2012). Extracting informative variables in the validation of two-group causal relationship. Computational Statistics, 28(3), 1151-1167. Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin:Springer-Verlag.Murdoch, D., Chow, E. D.(2013). ellipse: Functions for drawing ellipses and ellipse-like confidence regions. R package version 0.3-8.Retrieved from http://CRAN.R-project.org/package=ellipsePierce, D. A., & Haugh, L. D. (1977). Causality in temporal systems: Characterization and a survey. Journal of econometrics, 5(3), 265-293.Ripley, B., Venables, B., Bates, D.M., Hornik, K., Gebhardt A, Firth, D. (2014). MASS: Support Functions and Datasets for Venables and Ripley`s MASS. R package version 7.3-33. Retrieved from http://CRAN.R-project.org/package=MASSSims, C. A. (1972). Money, income, and causality. The American Economic Review, 62(4), 540-552.Sims, C. A. (1980). Macroecomonics and reality. Journal of Econometrica, 48, 1-48.Srinivasan, S. and Bass, F.M (2001). Diagnosing competitive responsiveness: Disentangling retailer-induced and manufacturer induced actions. Paper presented at the MSI Conference on competitive responsiveness, Boston. Takada, H., & Bass, F. M. (1998). Multiple time series analysis of competitive marketing behavior. Journal of Business Research, 43(2), 97-107.Trapletti, A., Hornik, K., LeBaron, B. (2013). tseries: Time series analysis and computational finance. R package version 0.10-32. Retrieved from http://CRAN.R-project.org/package=tseriesTsai, M. T. M., & Sen, P. K. (1993). On the local optimality of optimal linear tests for restricted alternatives. Statistica Sinica, 3, 103-115. zh_TW
