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ForeComp  

Size-Power Tradeoff Visualization for Equal Predictive Ability of Two Forecasts
View on CRAN: Click here


Download and install ForeComp package within the R console
Install from CRAN:
install.packages("ForeComp")

Install from Github:
library("remotes")
install_github("cran/ForeComp")

Install by package version:
library("remotes")
install_version("ForeComp", "0.9.0")



Attach the package and use:
library("ForeComp")
Maintained by
Minchul Shin
[Scholar Profile | Author Map]
First Published: 2023-09-05
Latest Update: 2023-09-05
Description:
Offers a set of tools for visualizing and analyzing size and power properties of the test for equal predictive accuracy, the Diebold-Mariano test that is based on heteroskedasticity and autocorrelation-robust (HAR) inference. A typical HAR inference is involved with non-parametric estimation of the long-run variance, and one of its tuning parameters, the truncation parameter, trades off a 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 the finite-sample size and power, it works with the best approximating ARMA process to the given dataset. It informs the user how their choice of the truncation parameter performs and how robust the testing outcomes are.
How to cite:
Minchul Shin (2023). ForeComp: Size-Power Tradeoff Visualization for Equal Predictive Ability of Two Forecasts. R package version 0.9.0, https://cran.r-project.org/web/packages/ForeComp. Accessed 02 Apr. 2025.
Previous versions and publish date:
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Complete documentation for ForeComp
Functions, R codes and Examples using the ForeComp R package
Full ForeComp package functions and examples
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