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ACV  

Optimal Out-of-Sample Forecast Evaluation and Testing under Stationarity
View on CRAN: Click here


Download and install ACV package within the R console
Install from CRAN:
install.packages("ACV")

Install from Github:
library("remotes")
install_github("cran/ACV")

Install by package version:
library("remotes")
install_version("ACV", "1.0.2")



Attach the package and use:
library("ACV")
Maintained by
Filip Stanek
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-04-05
Latest Update: 2022-04-05
Description:
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) .
How to cite:
Filip Stanek (2022). ACV: Optimal Out-of-Sample Forecast Evaluation and Testing under Stationarity. R package version 1.0.2, https://cran.r-project.org/web/packages/ACV. Accessed 28 Oct. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited ACV R package
View ACV citation profile
Other R packages that ACV depends, imports, suggests or enhances
Complete documentation for ACV
Functions, R codes and Examples using the ACV R package
Some associated functions: estimateL . estimateLongRunVar . estimateRho . infoPhi . print.estimateL . print.testL . shiftMatrix . testL . tsACV . 
Some associated R codes: ShiftMatrix.R . estimateL.R . estimateLongRunVar.R . estimateRho.R . infoPhi.R . print.estimateL.R . print.testL.R . testL.R . tsACV.R .  Full ACV package functions and examples
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