Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics
证明了常用的样本外预测性能检验统计量与常规Wald统计量渐近等价，简化了递归样本外检验的计算，并推导了嵌套模型下极限分布的简单形式及其密度。
We demonstrate the asymptotic equivalence between commonly used test statistics for out-of-sample forecasting performance and conventional Wald statistics. This equivalence greatly simplifies the computational burden of calculating recursive out-of-sample test statistics and their critical values. For the case with nested models, we show that the limit distribution, which has previously been expressed through stochastic integrals, has a simple representation in terms of -distributed random variables and we derive its density. We also generalize the limit theory to cover local alternatives and characterize the power properties of the test.