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We introduce tests for multi-horizon superior predictive ability. Rather than comparing forecasts of different models at multiple horizons individually, we propose to jointly consider all relevant horizons within a forecast path. We define the concepts of uniform and average superior predictive ability. The former entails superior performance at each individual horizon, while the latter allows inferior performance at some horizons to be compensated by others. We show that the tests lead to more coherent conclusions, and have greater power to differentiate models than the single-horizon tests. We provide an extension of the Model Confidence Set to allow for multi-horizon comparison of more than two models. Simulations demonstrate appropriate size and high power. An illustration of the tests on a large set of macroeconomic variables demonstrates the empirical benefits of multi-horizon comparison.
