This is the official code repository for paper titled "Regularized estimation of high-dimensional factor-augmented vector autoregressive (FAVAR) models" (2020, Journal of Machine Learning Research), by Jiahe Lin and George Michailidis.
- Link to paper: https://jmlr.csail.mit.edu/papers/volume21/19-874/19-874.pdf
- To cite this work: Lin, J., Michailidis, G. (2020) Regularized estimation of high-dimensional factor-augmented vector autoregressive (FAVAR) models. Journal of Machine Learning Research, 21(117): 1–51.
@article{lin2020regularized, title={Regularized estimation of high-dimensional factor-augmented vector autoregressive {(FAVAR)} models}, author={Lin, Jiahe and Michailidis, George}, journal={The Journal of Machine Learning Research}, volume={21}, number={1}, pages={4635--4685}, year={2020}, publisher={JMLRORG} }
In this repository, we provide both Python
and R
implementation of the proposed two-stage methodology.
- For
Python
version, seedemo.ipynb
, whose source files are under./srcPy
- For
R
version, seeexample.R
, whose source files are under./srcR
Note that there is some difference in the behavior of Lasso
from sklearn in Python
and glmnet
from R
, due to the discrepancy in their respective underlying implementations. The original paper, when it was being developed, replied on the R
version.
- For questions on the implementation, contact Jiahe Lin [jiahelin AT umich DOT edu]