This paper develops and analyzes a series of tests to evaluate the optimality of forecasts when forecasts for more than one horizon are available. The tests are based on the property that the unconditional expected loss of optimal forecasts should not decrease with the forecast horizon (e.g., under quadratic loss the variance of optimal forecast errors should not decrease with the horizon). The tests complement existing methods of forecast evaluation, such as Mincer-Zarnowitz-type tests, by using an implication of optimality that directly concerns forecasts made at different horizons. The finite sample performance of the tests is analyzed and an illustration using the Survey of Professional Forecasters is provided.
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