Package: PredictorSelect 0.1.0
Rong Peng
PredictorSelect: Out-of-Sample Predictability in Predictive Regressions with Many Predictor Candidates
Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.
Authors:
PredictorSelect_0.1.0.tar.gz
PredictorSelect_0.1.0.tar.gz(r-4.5-noble)PredictorSelect_0.1.0.tar.gz(r-4.4-noble)
PredictorSelect_0.1.0.tgz(r-4.4-emscripten)PredictorSelect_0.1.0.tgz(r-4.3-emscripten)
PredictorSelect.pdf |PredictorSelect.html✨
PredictorSelect/json (API)
| # Install 'PredictorSelect' in R: |
| install.packages('PredictorSelect', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:7060af3c35. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 08 2025 |
| R-4.5-linux | OK | Mar 08 2025 |
| R-4.4-linux | OK | Mar 08 2025 |
Exports:DMBAR_Testrecursive_hstep_fast
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Constructs the DMBAR Test statistic in GP2023 | DMBAR_Test |
| Forecasting h-steps ahead using Recursive Least Squares Fast | recursive_hstep_fast |