Reality Checks and Comparisons of Nested Predictive Models
提出一种简单的自助法来模拟渐近临界值，用于检验多个嵌套模型的预测精度和包含关系，蒙特卡洛实验表明该方法在小样本下表现优于其他方法。
This article develops a simple bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. Our method combines elements of fixed regressor and wild bootstraps. We first derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to forecasts from multiple models that nest the benchmark model—that is, reality check tests. We then prove the validity of the bootstrap for these tests. Monte Carlo experiments indicate that our proposed bootstrap has better finite-sample size and power than other methods designed for comparison of nonnested models. Supplementary materials are available online.