Description
Connectedness Approach.
Description
The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.
README.md
ConnectednessApproach
Step 1: Install the devtools package
To install a R package, start by installing the devtools package. The best way to do this is from CRAN, by typing:
install.packages("devtools")
Step 2: Install the package of interest from GitHub
Install the package of interest from GitHub using the following code, where you need to remember to list both the author and the name of the package (in GitHub jargon, the package is the repo, which is short for repository). In this example, we are installing the ConnectednessApproach package created by GabauerDavid.
library(devtools)
install_github("GabauerDavid/ConnectednessApproach")
Step 3: Go through tutorial
ConnectednessApproach Tutorial
BibTeX Citation
If you use this package in a scientific publication, I would appreciate if you use the following citation:
@article{gabauer2022,
title={Package ‘ConnectednessApproach’},
author={Gabauer, David},
year={2022}
}