Correlation‐based tests of predictability

Pablo Pincheira and Nicolas Hardy

Journal of Forecasting, 2024, vol. 43, issue 6, 1835-1858

Abstract: In this paper, we propose a correlation‐based test for the evaluation of two competing forecasts. Under the null hypothesis of equal correlations with the target variable, we derive the asymptotic distribution of our test using the Delta method. This null hypothesis is not necessarily equivalent to the null of equal Mean Squared Prediction Errors (MSPE). Specifically, it might be the case that the forecast displaying the lowest MSPE also exhibits the lowest correlation with the target variable: this is known as “The MSPE paradox.” In this sense, our approach should be seen as complementary to traditional tests of equality in MSPE. Monte Carlo simulations indicate that our test has good size and power. Finally, we illustrate the use of our test in an empirical exercise in which we compare two different inflation forecasts for a sample of OECD economies. We find more rejections of the null of equal correlations than rejections of the null of equality in MSPE.

Date: 2024
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https://doi.org/10.1002/for.3081

Related works:
Working Paper: Correlation Based Tests of Predictability (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:43:y:2024:i:6:p:1835-1858

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