Comparing predictive ability in presence of instability over a very short time

TL;DR

This paper examines the challenge of comparing forecast accuracy during brief periods of economic instability, proposing nonparametric methods better suited to detect short-lived shocks, with practical applications to US GDP nowcasting during Covid-19.

Contribution

It introduces and advocates for nonparametric testing approaches to improve forecast comparison during short-term instabilities, addressing limitations of traditional global tests.

Findings

Global tests lack power during short-lived shocks.

Nonparametric methods like S test and MAX are more effective.

Excluding unstable periods improves forecast evaluation accuracy.

Abstract

We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case, as they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss and propose approaches that are more suitable to detect such situations, such as nonparametric methods (S test or MAX procedure). We illustrate these results in different Monte Carlo exercises and in evaluating the nowcast of the quarterly US nominal GDP from the Survey of Professional Forecasters (SPF) against a naive benchmark of no growth, over the period that includes the GDP instability brought by the Covid-19 crisis. We recommend that the forecaster should not pool the sample, but exclude the short periods of high local instability from the evaluation exercise.