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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
  • CONTACT
Picture

Chapter 2: Classic Nonlinear Models

Data Sets
Chapter-2-data.zip
Jokulsa.dat
Oldman-river.dat
USunemplmnt_logistic.dat
USunemplmnt_matrix.dat
Computer Codes
​Examples:
Example_2-8.zip
Example_2-9.zip   
​
Exercises:
Exercise_2-9.zip    
Exercise_2-10.zip   
Exercise_2-11.zip   
Exercise_2-12.zip   

Miscellanea:
Gonzalo-Wolf-SETAR.zip  
GRASP.zip
SEASETAR.zip   

​​​Figures 
Figures-Chapter-2-exercises.zip         
Figures-Chapter-2_exercises-jpg.zip  
​

(M code)
(F code and renamed exe file)


(R code)
​(R code)
(R code)
​(M code)


​(C code)
(M code)
(F code)

​

(EPS format)
​(JPEG format)
​Links to  Websites with  Supplementary Material
  • Click on the following link for getting access to computer codes (Ox, MATLAB, and R) and papers related to a class of observation-driven nonlinear time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function;  http://www.gasmodel.com/background.htm.
  • Click on the following link for getting access to Marcelo Perlin’s website with MATLAB and R code for estimating Markov regime switching models. The R-MSGARCH package allows the user to perform ML and Bayesian estimation as well as forecasting for a very large class of models.
  • Click on the following link for getting access to James Hamilton’s website with data and software (mainly GAUSS) from various studies.
  • Click on the following link for getting access to the NNSYSID toolbox (system identification with NNs) for MATLAB, and the NNCTRL toolkit, an add-on for design and simulation of NNs based control systems.
  • Click on the following link for getting access to the website accompanying the book by Zivot and  Wang (2006), Modeling Financial Time Series with S-Plus (2nd. ed.), Springer-Verlag, New York.  Alternatively, R scripts are available at: http://faculty.washington.edu/ezivot/MFTSR.htm.
  • Click on the following link for getting access to Simon van Norden's homepage with GAUSS codes and data sets for analyzing switching regressions and Markov mixture models. The website econpapers.repec.org/paper/bcabocawp/96-3.htm provides documentation/information about the  computer programs. 
  • Data and GAUSS computer programs to accompany Kim and Nelson (1999),  ​​State-Space Models with Regime-Switching: Classical and Gibbs-sampling approaches with applications (MIT Press) can be downloaded from: econ.korea.ac.kr/~cjkim/MARKOV/prgmlist.htm. 
  • Click on the following link for getting access to Xiaming Huo's website with MATLAB and R codes for estimating a Hessian regularized nonlinear time series model introduced in Chen and Huo (2009, J. Computational and Graphical Statistics).
  • The website unstarched is the  home of a number of financial R-packages developed by Alexios Ghalanos. The  R-twinkle package (beta version)  includes steps for specifying, estimating, testing, and forecasting STAR(MA)  models.
Last modified: February, 2022

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