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 < at>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README
CRAN checks: ACV results


Reference manual: ACV.pdf


Package source: ACV_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ACV_1.0.2.tgz, r-oldrel (arm64): ACV_1.0.2.tgz, r-release (x86_64): ACV_1.0.2.tgz, r-oldrel (x86_64): ACV_1.0.2.tgz


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