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

Minchul Shin, Nathan Schor

arXiv 8 Mar 2026 · Econometrics

arXiv:2603.07458 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Lazarus, E., Lewis, D. J., Stock, J. H., and Watson, M. W (2018) HAR inference: Recommendations for practice1.00084100%
2Coroneo, L. and Iacone, F (2020) Comparing predictive accuracy in small samples using fixed-smoothing asymptotics0.9619689%
3Newey, W. K. and West, K. D (1994) Automatic lag selection in covariance matrix estimation0.92843100%
4Stark, T (2010) Realistic evaluation of real-time forecasts in the Survey of Professional Forecasters0.87452100%
5Kiefer, N. M. and Vogelsang, T. J (2005) A new asymptotic theory for heteroskedasticity-autocorrelation robust tests0.73732100%
6Diebold, F. X. and Mariano, R. S (1995) Comparing predictive accuracy0.64422100%
7Harvey, D., Leybourne, S., and Newbold, P (1997) Testing the equality of prediction mean squared errors0.64422100%
8McCracken, M. W (2019) Tests of conditional predictive ability: Some simulation evidence0.64422100%
9Sun, Y (2013) A heteroskedasticity and autocorrelation robust $F$ test using an orthonormal series variance estimator0.64422100%
10Canay, I. A., Romano, J. P., and Shaikh, A. M (2017) Randomization tests under an approximate symmetry assumption0.40511100%

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination $ $0.51132