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ForeComp

Size-Power Tradeoff Visualization for Equal Predictive Ability of Two Forecasts

v1.0.0 · Feb 20, 2026 · GPL (>= 3)

Description

Offers tools for visualizing and analyzing size and power properties of tests for equal predictive accuracy, including Diebold-Mariano and related procedures. Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) <doi:10.1002/jae.2756>, alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) <doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. 'ForeComp' computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies forecast astsa ggplot2 rlang ForeComp

Version History

new 1.0.0 Mar 9, 2026