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