Forecast Evaluation of Small Nested Model Sets
提出两种新方法，用于同时比较基准模型与多个嵌套替代模型的均方预测误差，无需自举重估，模拟显示调整MSPE差异的统计量更准确，并以美国通胀预测为例说明。
Abstract We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do not require re‐estimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West ( 2007 ); one procedure then examines the maximum t ‐statistic, while the other computes a chi‐squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi‐squared statistic and White's ( 2000 ) reality check. In these simulations, the two statistics that adjust MSPE differences have the most accurate size, and the procedure that looks at the maximum t ‐statistic has the best power. We illustrate our procedures by comparing forecasts of different models for US inflation. Copyright © 2010 John Wiley & Sons, Ltd.