Package: pretest 0.2
Rong Peng
pretest: A Novel Approach to Predictive Accuracy Testing in Nested Environments
This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.
Authors:
pretest_0.2.tar.gz
pretest_0.2.tar.gz(r-4.5-noble)pretest_0.2.tar.gz(r-4.4-noble)
pretest_0.2.tgz(r-4.4-emscripten)pretest_0.2.tgz(r-4.3-emscripten)
pretest.pdf |pretest.html✨
pretest/json (API)
| # Install 'pretest' in R: |
| install.packages('pretest', 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 1 years agofrom:e28b894289. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 16 2024 |
| R-4.5-linux | OK | Nov 16 2024 |
Exports:dm_cwlr_varNested_Stats_S0Nested_Stats_Sbarrecursive_hstep_fastrecursive_hstep_slow
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Diebold-Mariano Test and Clark & West Test | dm_cw |
| Heteroskedastic Long run variance | lr_var |
| Predictive Accuracy Testing for Nested Environment S^0 | Nested_Stats_S0 |
| Predictive Accuracy Testing for Nested Environment SBar | Nested_Stats_Sbar |
| Forecasting h-steps ahead using Recursive Least Squares Fast | recursive_hstep_fast |
| Forecasting h-steps ahead using Recursive Least Squares Slow | recursive_hstep_slow |