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ACV – package for 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 can be shown to be 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 “Optimal Out-of-Sample Forecast Evaluation Under Stationarity” by Filip Staněk.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.