midasml
midasml package is dedicated to run predictive high-dimensional mixed data sampling models
Science Score: 13.0%
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Low similarity (12.7%) to scientific vocabulary
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Repository
midasml package is dedicated to run predictive high-dimensional mixed data sampling models
Basic Info
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- Stars: 41
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- Forks: 23
- Open Issues: 3
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Metadata Files
README.md
midasml
midasml - Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series and Panel Data
About
The midasml package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO estimator. For more information on the midasml approach see [^1][^2][^3].
The package is equipped with the fast implementation of the sparse-group LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
Software in other languages
- Julia implmentation of the midasml method is available here.
- MATLAB implmentation of the midasml method is available here.
- Python implmentation of the midasml method is being developed at here.
Run to install the package
```{r }
CRAN version - 0.1.10
install.packages("midasml")
Development version - 0.1.10
install.packages("devtools")
library(devtools) install_github("jstriaukas/midasml") ```
Acknowledgements
Jonas Striaukas acknowledges that this material is based upon work supported by the Fund for Scientific Research-FNRS (Belgian National Fund for Scientific Research) under Grant #FC21388.
References
[^1]: Babii, A., Ghysels, E., & Striaukas, J. Machine learning time series regressions with an application to nowcasting, (2022) Journal of Business & Economic Statistics, Volume 40, Issue 3, 1094-1106. https://doi.org/10.1080/07350015.2021.1899933.
[^2]: Babii, A., Ghysels, E., & Striaukas, J. High-dimensional Granger causality tests with an application to VIX and news, (2022) Journal of Financial Econometrics, Forthcoming.
[^3]: Babii, A., R. Ball, Ghysels, E., & Striaukas, J. Machine learning panel data regressions with heavy-tailed dependent data: Theory and application, (2022) Journal of Econometrics, Forthcoming.
Owner
- Name: Jonas Striaukas
- Login: jstriaukas
- Kind: user
- Location: Copenhagen, Capital Region, Denmark
- Company: Copenhagen Business School
- Website: https://jstriaukas.github.io/
- Twitter: striaukas
- Repositories: 2
- Profile: https://github.com/jstriaukas
Assistant professor of statistics at Copenhagen Business School
GitHub Events
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- Watch event: 3
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Last Year
- Watch event: 3
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Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Jonas Striaukas | j****s@g****m | 106 |
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Last synced: 6 months ago
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- Total pull requests: 2
- Average time to close issues: about 1 month
- Average time to close pull requests: 4 days
- Total issue authors: 8
- Total pull request authors: 2
- Average comments per issue: 3.92
- Average comments per pull request: 2.0
- Merged pull requests: 0
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- jstriaukas (3)
- Yuanyuan77-wang (2)
- Bootcampanalytics (2)
- Beliavsky (1)
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- Denis9678 (1)
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- thierrymoudiki (1)
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Packages
- Total packages: 1
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Total downloads:
- cran 898 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 18
- Total maintainers: 1
cran.r-project.org: midasml
Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
- Documentation: http://cran.r-project.org/web/packages/midasml/midasml.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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Latest release: 0.1.10
published almost 4 years ago
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Maintainers (1)
Dependencies
- Matrix * depends
- R >= 3.5.0 depends
- doParallel * imports
- doRNG * imports
- foreach * imports
- graphics * imports
- lubridate * imports
- methods * imports
- randtoolbox * imports
- snow * imports
- stats * imports