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
DOI:	10.32614/CRAN.package.ACV
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:

Reference manual:

ACV.pdf

Downloads:

Package source:	ACV_1.0.2.tar.gz
Windows binaries:	r-devel: ACV_1.0.2.zip, r-release: ACV_1.0.2.zip, r-oldrel: ACV_1.0.2.zip
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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