Small-Sample Properties of GMM-Based Wald Tests
评估了基于广义矩方法的Wald统计量在小样本下的表现，发现其实际检验水平常超过渐近水平，且随联合检验假设数量增加而恶化，主要原因是残差谱密度矩阵估计困难，而施加模型或原假设约束的估计量能显著改善检验性质。
This article assesses the small-sample properties of generalized-method-of-moments-based Wald statistics by using (a) a vector white-noise process and (b) an equilibrium business-cycle model as the data-generating mechanisms. In many cases, the small-sample size of the Wald tests exceeds its asymptotic size and increases sharply with the number of hypotheses being jointly tested. We argue that this is mostly due to difficulty in estimating the spectral-density matrix of the residuals. Estimators of this matrix that impose restrictions implied by the model or the null hypothesis substantially improve the properties of the Wald statistics.