政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141003

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141003

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113451/144438 (79%)
造访人次 : 51320908 在线人数 : 792

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/141003

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/141003

题名:	以非樞紐統計量為基礎之格蘭傑因果關係檢定 Granger Causality Test Based on Non-pivotal Statistics
作者:	姚惠元 Yao, Huei-Yuan
贡献者:	洪英超 Hung, Ying-Chao 姚惠元 Yao, Huei-Yuan
关键词:	格蘭傑因果關係 Modified Wald 檢定非樞紐統計量向量自迴歸 Granger causality Modified Wald test Nonpivotal statistic Vector autoregression
日期:	2022
上传时间:	2022-08-01 17:14:43 (UTC+8)
摘要:	格蘭傑因果關係是一個透過結合向量自迴歸模型中所有變數的資訊於衡量兩組時間序列間可預測性的經典統計分析工具，傳統分析格蘭傑因果關係的推論方法為 Wald 類型的檢定方法，然而這些檢定方法可能會面臨以下問題: 一、需要挑選微調參數，二、當預估測之共變異數矩陣為奇異矩陣時，用於推論的臨界值會失效。在這篇論文中，我們發展了一個基於非樞紐統計量的格蘭傑因果關係檢定，此方法不僅避免了以上兩個問題，相較於 Wald 類型的檢定，我們的方法有更佳的檢定力，最後我們也通過幾個模擬例子和實際資料分析驗證此方法的有效性。 Granger causality is a classical tool for measuring predictability from one group of time series to another by incorporating information of variables described by a vector autoregressive (VAR) model. Traditional methods for validating Granger causality are based on the Wald type tests, which may encounter a problem with (i) tuning parameter selection or (ii) test-statistic inflation when the true covariance matrix is singular or near-singular. In this study, we propose an alternative procedure for testing Granger causality based on non-pivotal statistics. The proposed hypothesis testing method is valuable in that (i) it does not require any calibration of tuning parameters (thus saving huge computational cost); and (ii) it yields very competitive power values as compared with the Wald type tests. Finally, a number of simulation examples and a real data set are used to illustrate and evaluate the proposed method.
參考文獻:	Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected papers of hirotugu akaike, pages 199–213. Springer. Amblard, P.-O. and Michel, O. J. (2011). On directed information theory and granger causality graphs. Journal of computational neuroscience, 30(1):7–16. Anderson, T. W. (1963). Asymptotic theory for principal component analysis. The Annals of Mathematical Statistics, 34(1):122–148. Andrews, D. W. (1987). Asymptotic results for generalized wald tests. Econometric Theory, 3(3):348–358. Basu, S., Shojaie, A., and Michailidis, G. (2015). Network granger causality with inherent grouping structure. The Journal of Machine Learning Research, 16(1):417–453. Boudjellaba, H., Dufour, J.-M., and Roy, R. (1992). Testing causality between two vectors in multivariate autoregressive moving average models. Journal of the American Statistical Association, 87(420):1082–1090. Dagenais, M. G. and Dufour, J.-M. (1991). Invariance, nonlinear models, and asymptotic tests. Econometrica: Journal of the Econometric Society, pages 1601–1615. Davis, R. A., Zang, P., and Zheng, T. (2016). Sparse vector autoregressive modeling. Journal of Computational and Graphical Statistics, 25(4):1077–1096. Dufour, J.-M. (2003). Identification, weak instruments, and statistical inference in econometrics. Canadian Journal of Economics/Revue canadienne d’économique, 36(4):767– 808. Dufour, J.-M., Pelletier, D., and Renault, É. (2006). Short run and long run causality in time series: inference. Journal of Econometrics, 132(2):337–362. Dufour, J.-M. and Renault, E. (1998). Short run and long run causality in time series: theory. Econometrica, pages 1099–1125. Dufour, J.-M., Renault, E., and Zinde-Walsh, V. (2013). Wald tests when restrictions are locally singular. arXiv preprint arXiv:1312.0569. Ferguson, T. S. (2017). A course in large sample theory. Routledge. Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications. Granger, C. W. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica: journal of the Econometric Society, pages 424–438. Hannan, E. J. and Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41(2):190–195. Hung, Y.-C., Tseng, N.-F., and Balakrishnan, N. (2014). Trimmed granger causality between two groups of time series. Electronic Journal of Statistics, 8(2):1940–1972. Lozano, A. C., Abe, N., Liu, Y., and Rosset, S. (2009). Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics, 25(12):i110– i118. Lütkepohl, H. (2000). Bootstrapping impulse responses in var analyses. In COMPSTAT, pages 109–119. Springer. Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media. Lütkepohl, H. and Burda, M. M. (1997). Modified wald tests under nonregular conditions. Journal of Econometrics, 78(2):315–332. Quinn, B. G. (1980). Order determination for a multivariate autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 42(2):182–185. Ratsimalahelo, Z. (2005). Generalised wald type tests of nonlinear restrictions. IFAC Proceedings Volumes, 38(1):100–105. Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, pages 461–464. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, pages 1–48. Tank, A., Covert, I., Foti, N., Shojaie, A., and Fox, E. (2018). Neural granger causality. arXiv preprint arXiv:1802.05842. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288. Vicente, R., Wibral, M., Lindner, M., and Pipa, G. (2011). Transfer entropy—a modelfree measure of effective connectivity for the neurosciences. Journal of computational neuroscience, 30(1):45–67. Von Weyl, H. (1909). Über beschränkte quadratische formen, deren differenz vollstetig ist. Rendiconti del Circolo Matematico di Palermo (1884-1940), 27(1):373–392. Xiao, H. and Wu, W. B. (2012). Covariance matrix estimation for stationary time series. The Annals of Statistics, 40(1):466–493. Yuen, T., Wong, H., and Yiu, K. F. C. (2018). On constrained estimation of graphical time series models. Computational Statistics & Data Analysis, 124:27–52.
描述:	碩士國立政治大學統計學系 109354004
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109354004
数据类型:	thesis
DOI:	10.6814/NCCU202200767
显示于类别:	[統計學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
400401.pdf		1018Kb	Adobe PDF2	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈