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经济时间序列的非线性特性检验及其应用 被引量:2

Nonlinear Characteristic Examination of Economic Time Serial and Its Applications
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摘要 应用BDS统计量分析方法以及Lyapunov指数的分析方法研究经济时间序列的随机特性、混沌特性,应用R/S分析方法研究时间序列短期或长期依赖特性及其演化趋势,从而进一步探明时序所对应的系统的内在复杂性本质,为时序的建模提供理论依据.通过对3组不同的经济时序进行的实证研究,研究结果对于实测的经济时序内在本质特征的判定,有较为重要的理论和实际意义,为进一步寻找适合的非线性模型对经济时间序列进行有效分析和预测提供了理论依据. Applying the BDS statistics to measure analysis method and the Lyapunov exponent analysis method, we study the machine characteristic and mentally dense characteristic of economic time series. Applying the R/ S analysis method, we study the depending characteristic about the short-term or long-term time series and its evolvement trend. Consequently, we explore the inside complexity essential characteristics of the system of time series, which provides the theories groundwork for time serial molding. With the study on three different substantial evidences for economic time series, the research result has the important theories and actual meaning for the judgment about the essential characteristics of the solid economic time serial. It provides the theories groundwork for finding further valid analysis and forecast method about economic time series.
作者 莫馨 马军海
出处 《河北工业大学学报》 CAS 2004年第6期13-18,共6页 Journal of Hebei University of Technology
基金 国家自然科学基金资助项目(70271071)天津市教委资助项目(20041702)
关键词 经济时间序列 混沌系统 HURST指数 R/S分析方法 BDS统计量 LYAPUNOV指数 economic time series chaotic system Hurst exponent R/S analysis method BDS statistics Lyapunov exponent
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  • 1陈予恕,马军海,刘曾荣.THE STATE SPACE RECONSTRUCTION TECHNOLOGY OF DIFFERENT KINDS OF CHAOTIC DATA OBTAINED FROM DYNAMICAL SYSTEM[J].Acta Mechanica Sinica,1999,15(1):82-92. 被引量:4
  • 2马军海 陈予恕.高斯分布的随机数对动力系统实测数据判值影响的分析研究[J].非线性动力学学报,1997,4(1):25-33.
  • 3Zuo Binwu,Phys D,1995年,85卷,485页
  • 4Shun Guanwu,Phys Lett A,1995年,197卷,1期,287页
  • 5Yu Quanhe,Phys Lett A,1992年,170卷,1期,29页
  • 6Potapov Alexei.Distortions of reconstruction for chaotic attractors[].Physica D Nonlinear Phenomena.1997
  • 7Judd Kevin,Mees Alistair.Embedding as a modeling problem[].Physica D Nonlinear Phenomena.1998
  • 8Kugiumtzis D,Lingjxrde O C,Christophersen N.Regularized local linear prediction of chaotic time series[].Physica D Nonlinear Phenomena.1998
  • 9Abarbanel Henry D I,Brown Reggie,Kadtke James B.Prediction and system identification in chaotic nonlinear systems: time series with broadband spectra[].Physics Letters A.1989
  • 10Schroer Christian G,Sauer Tim,Ott Edvard,et al.Predicting chaotic most of the time from embeddings with self_intersections[].Physical Review Letters.1998

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