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Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts

Akgun, O., Pirotte, A., Urga, G. ORCID: 0000-0002-6742-7370 & Yang, Z. (2024). Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts. International Journal of Forecasting, 40(1), pp. 202-228. doi: 10.1016/j.ijforecast.2023.02.001


We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, namely 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, called 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.

Publication Type: Article
Additional Information: © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Cross-Sectional Dependence, Forecast Evaluation, Hypothesis Testing
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
SWORD Depositor:
[thumbnail of APUY_06December2022_IJF_(Final).pdf] Text - Accepted Version
This document is not freely accessible until 8 March 2025 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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