The paper has been published online: jtsa.12561
- The data are in the folder data and are compressed R data files.
- The various PGAS files are in the folder R ... these are sourced in the files used to run the examples.
- Each example is identified by starting with
run_and then a self describing title. You just run the code, it will call the data file and PGAS procedure as needed. - Added an example from stochvol R package, but the essential part of the code is in one of the vignettes.
You'll need the following R packages to run all the code:
- astsa
- plyr
- MASS
- mcmc
- stochvol (needed only to run their example, figure 2)
The bibTeX entry for the current version is:
@article{doi:10.1111/jtsa.12561,
author = {Gong, Chen and Stoffer, David S.},
title = {A Note on Efficient Fitting of Stochastic Volatility Models},
journal = {Journal of Time Series Analysis},
year = {2021},
volume = {42},
number = {2},
pages = {186-200},
keywords = {Ancestral sampling, efficient Markov chain Monte Carlo, particle Gibbs, stochastic volatility},
doi = {10.1111/jtsa.12561},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsa.12561},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/jtsa.12561},
}
And plain text is
Gong, C. and Stoffer, D.S. (2021), A Note on Efficient Fitting of Stochastic Volatility Models.
J. Time Ser. Anal., 42: 186-200. https://doi.org/10.1111/jtsa.12561
For the bibTeX item to the code here, I used the following:
@misc{GitGongStoffer2020,
author = {Gong, Chen and Stoffer, David S.},
title = {{Stochastic Volatility Models}},
howpublished = "\url{https://github.com/nickpoison/Stochastic-Volatility-Models/}",
month = {09},
year = {2020},
note = "[GitHub Repository]"
}