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  "Title": "Size-Power Tradeoff Visualization for Equal Predictive Ability\nof Two Forecasts",
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  "Description": "Offers tools for visualizing and analyzing size and power\nproperties of tests for equal predictive accuracy, including\nDiebold-Mariano and related procedures. Provides multiple\nDiebold-Mariano test implementations based on fixed-smoothing\napproaches, including fixed-b methods such as Kiefer and\nVogelsang (2005) <doi:10.1017/S0266466605050565>, and\napplications to tests for equal predictive accuracy as in\nCoroneo and Iacone (2020) <doi:10.1002/jae.2756>, alongside\nconventional large-sample approximations. HAR inference\ninvolves nonparametric estimation of the long-run variance, and\na key tuning parameter (the truncation parameter) trades off\nsize and power. Lazarus, Lewis, and Stock (2021)\n<doi:10.3982/ECTA15404> theoretically characterize the\nsize-power frontier for the Gaussian multivariate location\nmodel. 'ForeComp' computes and visualizes the finite-sample\nsize-power frontier of the Diebold-Mariano test based on\nfixed-b asymptotics together with the Bartlett kernel. To\ncompute finite-sample size and power, it fits a best\napproximating ARMA process to the input data and reports how\nthe truncation parameter performs and how robust testing\noutcomes are to its choice.",
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