The preprint is available at: arXiv or Research Gate
- 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. - We changed the simulation a bit (Figures 1 & 2) but the preprint mentioned above still has the old figures - there's not much difference but we found a little blooper in the orignal version. I put the latest version up on my homepage.
You'll need the following R packages to run all the code:
- astsa
- plyr
- MASS
- mcmc
The bibTeX item for the preprint at arXiv can be:
@online{GongStoffer2019,
author = {Gong, Chen and Stoffer, David S.},
year = {2019},
month = {07},
title = {An Approach to Efficient Fitting of Univariate and Multivariate Stochastic Volatility Models},
doi = {10.13140/RG.2.2.29926.37440}
howpublished = "\url{https://arxiv.org/abs/1907.08372}",
}
or at Research Gate:
@online{GongStoffer2019,
author = {Gong, Chen and Stoffer, David S.},
year = {2019},
month = {07},
title = {An Approach to Efficient Fitting of Univariate and Multivariate Stochastic Volatility Models},
doi = {10.13140/RG.2.2.29926.37440}
howpublished = "\url{https://www.researchgate.net/publication/334457681_An_Approach_to_Efficient_Fitting_of_Univariate_and_Multivariate_Stochastic_Volatility_Models}",
}
For the bibTeX item to the code here, I used the following:
@misc{GitGongStoffer2019,
author = {Gong, Chen and Stoffer, David S.},
title = {{Stochastic Volatility Models}},
howpublished = "\url{https://github.com/nickpoison/Stochastic-Volatility-Models/}",
year = {2019},
note = "[GitHub Repository]"
}