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ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics

Minchul Shin, Nathan Schor

TL;DR

ForeComp, an R package for comparing predictive accuracy using Diebold-Mariano type tests of equal predictive ability with standard and fixed smoothing inference, is introduced and illustrated with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance.

Abstract

We introduce ForeComp, an R package for comparing predictive accuracy using Diebold-Mariano type tests of equal predictive ability with standard and fixed smoothing inference. The package provides a common interface for loss differential based testing and includes Plot Tradeoff, a visual diagnostic for bandwidth sensitivity and the size-power tradeoff. We illustrate the toolkit with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance.

ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics

TL;DR

ForeComp, an R package for comparing predictive accuracy using Diebold-Mariano type tests of equal predictive ability with standard and fixed smoothing inference, is introduced and illustrated with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance.

Abstract

We introduce ForeComp, an R package for comparing predictive accuracy using Diebold-Mariano type tests of equal predictive ability with standard and fixed smoothing inference. The package provides a common interface for loss differential based testing and includes Plot Tradeoff, a visual diagnostic for bandwidth sensitivity and the size-power tradeoff. We illustrate the toolkit with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance.
Paper Structure (33 sections, 17 equations, 2 figures, 46 tables)

This paper contains 33 sections, 17 equations, 2 figures, 46 tables.

Table of Contents

  1. Introduction
  2. Framework: Comparing Two Forecasts
  3. Testing Procedures in ForeComp
  4. Standard Procedures
  5. DM-R: Original DM test (dm.test.r).
  6. DM-M: Modified DM test (dm.test.r.m).
  7. DM-NW: Bartlett kernel with normal approximation (dm.test.bt).
  8. Fixed-Smoothing and Alternative Procedures
  9. DM-FB: Bartlett kernel with fixed-$b$ asymptotics (dm.test.bt.fb).
  10. DM-EWC: Equal-weighted cosine estimator (dm.test.ewc.fb).
  11. DM-WPE: Weighted periodogram estimator with fixed-$m$ asymptotics (dm.test.wpe.fb).
  12. DM-IM: Ibragimov--Müller test (dm.test.im).
  13. Package Usage
  14. Installation
  15. Common Interface
  16. ...and 18 more sections

Figures (2)

  • Figure 1: Size--power tradeoff: Real output growth, $h = 0$, 2007:Q1--2016:Q4 ($T = 40$). Each point corresponds to a bandwidth $M$. Red circles ($\bullet$): H0 not rejected by WCE-B. Red crosses ($\times$): H0 rejected. Green marker: package default $M = \lceil 1.3\sqrt{T} \rceil = 9$.
  • Figure 2: Size--power tradeoff: Unemployment, $h = 4$, $T = 40$. Each point corresponds to a bandwidth $M$. Red circles ($\bullet$): H0 not rejected by WCE-B. Red crosses ($\times$): H0 rejected. Green marker: package default $M = \lceil 1.3\sqrt{T} \rceil = 9$.