单位根过程与分整过程间变化变点的Sieve Bootstrap检验
Sieve Bootstrap Test for Changes between Unit Root Process and Fractional Integrated Processes
DOI: 10.12677/SA.2018.72014, PDF,    国家自然科学基金支持
作者: 何明灿*, 付国龙：青海师范大学数学与统计学院，青海 西宁
关键词: 单位根过程；分整过程；变点；Sieve Bootstrap；Unit Root Process； Fractional Integrated Process； Changes Point； Sieve Bootstrap
摘要: 本文基于Dickey-Full比率统计量研究了从单位根过程向分整过程变化变点，以及从分整过程向单位根过程变化变点的检验问题，并提出了用于确定检验统计量临界值的Sieve Bootstrap方法，数值模拟结果表明本文方法在单位根及分整过程原假设下都能很好的控制检验水平，在变点位置不是太靠后时，对两种备择假设下的变点都有较高的检验势，且在检验从单位根过程向分整过程变化变点时效果更显著。
Abstract: This paper aimed to test change point from unit root process to fractional integrated process as well as fractional integrated process to unit process via a Dickey-Full ratio statistic. A Sieve Bootstrap method was proposed to determine the critical values. Simulations indicate that our proposed method can control the empirical size well both under the unit root and fractional integrated process null hypotheses, and gives satisfy empirical powers under two alternative hypotheses if the change point location does not too back. Furthermore, Dickey-Full ratio statistic has better performance when detecting those changes which from unit root process to fractional integrated process.
文章引用：何明灿, 付国龙. 单位根过程与分整过程间变化变点的Sieve Bootstrap检验[J]. 统计学与应用, 2018, 7(2): 111-116. https://doi.org/10.12677/SA.2018.72014

参考文献

[1] Hakkio, A.W. and Rush, M. (1991) Is the Budget Deficit Too Large? Econometrics Inquiry, 29, 429-445.
[2] Kim, J.Y. (2000) Detection of Change in Persistence of Linear Times Series. Journal of Econometrics, 95, 97-116. [Google Scholar] [CrossRef] 
[3] Kim, J.Y., Belaire Franch, J. and Badilli Amador, R. (2002) Corri-gendum to “Detection of Change in Persistence of Linear Times Series”. Journal of Econometrics, 109, 389-392. [Google Scholar] [CrossRef] 
[4] Perron, P. (1989) The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis. Econometrica, 57, 1361-1401. [Google Scholar] [CrossRef] 
[5] Perron, P. (1990) Testing for a Unit Root in a Time Series with a Changing Mean. Journal of Business & Economic Statistics, 8, 153-162.
[6] Anindya, Banerjee, Lumsdaine, R. and Stock, J. (1992) Recursive and Sequential Tests of the Unit-Root and Trend-Break Hypotheses: Theory and International Evidence. Journal of Business & Economic Statistics, 10, 271-287.
[7] Leybourne, S.J., Kim, T.H. and Taylor, A.M.R. (2006) Regression-Based Tests for a Change in Persistence. Oxford Bulletin of Economics & Statistics, 68, 595-621. [Google Scholar] [CrossRef] 
[8] Hassler, U. and Scheithauer, J. (2011) Detecting Changes from Short to Long Memory. Statistical Paper, 52, 847-870. [Google Scholar] [CrossRef] 
[9] Sibbertsen, P. and Kruse, R. Testing for a Break in Persistence under Long-Range Dependencies.
[10] Buhlmann, P. (1997) Sieve Bootstrap for Time Series. Bernoulli, 3, 123-148. [Google Scholar] [CrossRef] 
[11] Poskitt, D.S. (2008) Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes. Journal of Time Series Analysis, 29, 224-250. [Google Scholar] [CrossRef] 
[12] Kapetanios, G. (2010) A Generalization of a Sieve Bootstrap In-variance Principle to Long Memory Processes. Quantitative and Qualitative Analysis in Social Sciences, 4, 19-40.
[13] Chen, Z., Tian, Z., Xiao, Q. and Xing, Y. (2014) Sieve Bootstrap Test for Variance Change in Long Memory Time Series. International Joint Conference on Applied Mathematics, 41, 61-73.
[14] Chen, Z., Tian, Z. and Xing, Y. (2016) Sieve Bootstrap Monitoring Per-sistence Change in Long Memory Process. Statistics & Its Interface, 9, 37-45. [Google Scholar] [CrossRef] 
[15] Chen, Z., Xing, Y. and Li, F. (2016) Sieve Bootstrap Monitoring for Change from Short to Long Memory. Economics Letters, 140, 53-56. [Google Scholar] [CrossRef] 
[16] Rambaccussing, D. (2015) A Test of the Long Memory Hypothesis Based on Self-Similarity. Journal of Time Series Econometrics, Sire Discussion Papers, 7.
[17] Robinson, P.M. (1995) Gaussian Semiparametric Estimation of Long Range Dependence. Annals of Statistics, 23, 1630-1661. [Google Scholar] [CrossRef] 

为你推荐