Package: ForeComp Type: Package Title: Size-Power Tradeoff Visualization for Equal Predictive Ability of Two Forecasts Version: 1.0.0 Author: Nathan Schor [aut], Minchul Shin [aut, cre, cph] Maintainer: Minchul Shin Authors@R: c( person("Nathan", "Schor", role = "aut"), person("Minchul", "Shin", email = "visiblehand@gmail.com", role = c("aut", "cre", "cph")) ) 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) , and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) , 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) 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. License: GPL (>= 3) Encoding: UTF-8 URL: https://github.com/mcmcs/ForeComp BugReports: https://github.com/mcmcs/ForeComp/issues LazyData: true Depends: R (>= 3.5.0), stats Imports: forecast, astsa, ggplot2, rlang Suggests: testthat (>= 3.0.0) Config/testthat/edition: 3 RoxygenNote: 7.3.3 Repository: https://mcmcs.r-universe.dev Date/Publication: 2026-02-21 01:21:40 UTC RemoteUrl: https://github.com/mcmcs/forecomp RemoteRef: HEAD RemoteSha: b488a5a40d6cd988d8d3287badeeef5ceef2ed57 NeedsCompilation: no Packaged: 2026-06-21 06:53:07 UTC; root