Path forecast evaluation
提出用联合预测密度和Scheffé方法构建路径预测的联合置信区域，解决传统边际误差带覆盖不准的问题，并通过美国加息案例展示应用。
Abstract A path forecast refers to the sequence of forecasts 1 to H periods into the future. A summary of the range of possible paths the predicted variable may follow for a given confidence level requires construction of simultaneous confidence regions that adjust for any covariance between the elements of the path forecast. This paper shows how to construct such regions with the joint predictive density and Scheffé's S‐method of 1953. In addition, the joint predictive density can be used to construct simple statistics to evaluate the local internal consistency of a forecasting exercise of a system of variables. Monte Carlo simulations demonstrate that these simultaneous confidence regions provide approximately correct coverage in situations where traditional error bands, based on the collection of marginal predictive densities for each horizon, are vastly off the mark. The paper showcases these methods with an application to the most recent monetary episode of interest rate hikes in the US macroeconomy. Copyright © 2010 John Wiley & Sons, Ltd.