Forecast Rationality Tests Based on Multi-Horizon Bounds
提出基于平方误差损失下预测矩约束的理性检验方法，包括无需目标变量数据的检验，并通过回归不等式约束和蒙特卡洛模拟评估其效果，应用于美联储绿皮书预测。
Forecast rationality under squared error loss implies various bounds on second moments of the data across forecast horizons. For example, the mean squared forecast error should be increasing in the horizon, and the mean squared forecast should be decreasing in the horizon. We propose rationality tests based on these restrictions, including new ones that can be conducted without data on the target variable, and implement them via tests of inequality constraints in a regression framework. A new test of optimal forecast revision based on a regression of the target variable on the long-horizon forecast and the sequence of interim forecast revisions is also proposed. The size and power of the new tests are compared with those of extant tests through Monte Carlo simulations. An empirical application to the Federal Reserve's Greenbook forecasts is presented.