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  • Home
  • Publications
  • Springer Book
    • Table of Content
    • Chapter 1
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7
    • Chapter 8
    • Chapter 9
    • Chapter 10
    • Chapter 11
    • Chapter 12
    • Errata
    • Reviews
  • DATA SETS
  • Announcement
  • CONTACT
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Chapter 7: Tests for Serial Dependence

Data Sets
Chapter-7-data.zip
EpilepsyMR.dat
First-diff-USunemplmnt-n183.dat
Hare_n30_1905_1934.dat
Lynx.dat
Lynx_n30_1905_1934.dat
Lynx-n114-log10.dat
SP500.dat
SP500_n218.dat
SP500_n608.dat
​​Computer Codes
Examples:
Example 7.6 and Section 7.5:
​      Rank-based-BDS.zip

​Section 7.3.3:
     Bagnato-et-al.zip
     Hong.zip
     Hong-Lee.zip
     Hong-White.zip  
     Skaug-Tjostheim.zip
     
Exercise:
​Exercise_7-7.zip 

Figures 
Figures-Chapter-7-exercises.zip         
Figures-Chapter-7-exercises-jpg.zip   


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(C code)


(R code)
(G code)
(G code)
(G code)
(C code)
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​(M code)

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​(EPS format)
(JPEG format)
​Links to  Websites with  Supplementary Material
  • Click on the following link for getting access to Bruno  Rémillard's homepage with MATLAB and R codes for financial engineering (nonlinear time series) applications.
  • Click on the following link for getting access to C source code for the BDS test statistic for serial independence; see also Blake LeBaron's homepage.
  • Appendix C of the paper by Ludwig Kanzler entitled "Very fast and correctly sized estimation of the BDS statistic" contains fast MATLAB codes  for computing the BDS test statistic.
  • Dimitris Kugiumtzis homepage contains MATLAB code for the generation of surrogate time series with the algorithm of Statistically Transformed Autoregressive Process (STAP); see, e.g., Physical Review E, 2002, Vol. 66. The paper in  Stud. Nonlinear Dyn. E. 2008,  comes with MATLAB scripts for the generation of surrogate and bootstrap data and three test statistics for nonlinearity (authorized access).
  • Giannerini et al. (2015, Biometrika} propose  test statistics for pairwise nonlinear dependence under the null hypothesis of general linear dependence. The  R-package that implements these test statistics is available here or from Simone Giannerini's homepage.

Last modified: November, 2020

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