Modelling methodology and forecast failure
通过模拟分析模型选择策略（如从一般到具体）对预测表现的影响，发现这些策略不会导致严重过度拟合或预测失败率大幅超过名义水平，参数非恒定则凸显正确设定的重要性。
We analyse by simulation the impact of model-selection strategies (sometimes called pre-testing) on forecast performance in both constant-and non-constant-parameter processes. Restricted, unrestricted and selected models are compared when either of the first two might generate the data. We find little evidence that strategies such as general-to-specific induce significant over-fitting, or thereby cause forecast-failure rejection rates to greatly exceed nominal sizes. Parameter non-constancies put a premium on correct specification, but in general, model-selection effects appear to be relatively small, and progressive research is able to detect the mis-specifications.