ACV: Optimal Out-of-Sample Forecast Evaluation and Testing under
Stationarity
Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.
| Version: |
1.0.2 |
| Imports: |
forecast, Matrix, methods, stats |
| Suggests: |
testthat |
| Published: |
2022-04-05 |
| Author: |
Filip Stanek [aut, cre] |
| Maintainer: |
Filip Stanek <stanek.fi at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
ACV results |
Documentation:
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