论文标题
bootstrap辅助的自称方法，用于合并回归中的推理
A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions
论文作者
论文摘要
协调回归中的传统推论需要调整参数选择才能估算长期差异参数。即使这些选择是“最佳”，测试也会严重变形。我们提出了一种新型的自称方法，该方法可导致滋扰参数的无限限制分布，而无需直接估算长期差异参数。这使我们的自称测试调谐参数不含，并且只能以小功率损失为代价而容易造成尺寸扭曲。结合渐近矢量自回归筛引导程序以构建临界值，当误差序列相关或回归器内生性水平较大时，自称方法在中小型样本中显示出进一步的改善。我们通过分析德国和美国的Fisher效应的有效性来说明自举辅助辅助自相规划测试的有用性。
Traditional inference in cointegrating regressions requires tuning parameter choices to estimate a long-run variance parameter. Even in case these choices are "optimal", the tests are severely size distorted. We propose a novel self-normalization approach, which leads to a nuisance parameter free limiting distribution without estimating the long-run variance parameter directly. This makes our self-normalized test tuning parameter free and considerably less prone to size distortions at the cost of only small power losses. In combination with an asymptotically justified vector autoregressive sieve bootstrap to construct critical values, the self-normalization approach shows further improvement in small to medium samples when the level of error serial correlation or regressor endogeneity is large. We illustrate the usefulness of the bootstrap-assisted self-normalized test in empirical applications by analyzing the validity of the Fisher effect in Germany and the United States.
