Equal predictive ability tests based on panel data with applications to OECD and IMF forecasts

Oguzhan Akgun, Alain Pirotte, Giovanni Urga and Zhenlin Yang

International Journal of Forecasting, 2024, vol. 40, issue 1, 202-228

Abstract: We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, S-statistics, focuses on the overall EPA hypothesis, which states that the EPA holds, on average, over all panel units and over time. The second type, C-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties, even with little information about the cross-sectional dependence in the data. The proposed framework is applied to compare the economic growth forecasts of the OECD and the IMF, and to evaluate the performance of the consumer price inflation forecasts of the IMF.

Keywords: Cross-sectional dependence; Forecast evaluation; Hypothesis testing (search for similar items in EconPapers)
Date: 2024
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Working Paper: Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:1:p:202-228

DOI: 10.1016/j.ijforecast.2023.02.001

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