论文标题
本地投影推断比您想象的要简单，更强大
Local Projection Inference is Simpler and More Robust Than You Think
论文作者
论文摘要
应用的宏观经济学家通常使用局部预测（即对当前协变量的未来结果的直接线性回归，即直接的线性回归来计算置信区间。本文证明，局部投影推断可鲁棒处理应用程序中通常出现的两个问题：高度持久的数据和远距离脉冲响应的估计。我们考虑控制回归中变量滞后的局部预测。我们表明，具有正常临界值的滞后局部预测在（i）固定数据和非平稳数据上均无均匀地有效，也超过（ii）（ii）广泛的响应范围。此外，滞后增强可以消除需要纠正回归残差中串行相关性的标准误差的必要性。因此，可以说，局部投影推断既比以前想象的要简单，而且比标准自回归推断更简单，而且众所周知，其有效性敏感地取决于数据的持久性和地平线的长度。
Applied macroeconomists often compute confidence intervals for impulse responses using local projections, i.e., direct linear regressions of future outcomes on current covariates. This paper proves that local projection inference robustly handles two issues that commonly arise in applications: highly persistent data and the estimation of impulse responses at long horizons. We consider local projections that control for lags of the variables in the regression. We show that lag-augmented local projections with normal critical values are asymptotically valid uniformly over (i) both stationary and non-stationary data, and also over (ii) a wide range of response horizons. Moreover, lag augmentation obviates the need to correct standard errors for serial correlation in the regression residuals. Hence, local projection inference is arguably both simpler than previously thought and more robust than standard autoregressive inference, whose validity is known to depend sensitively on the persistence of the data and on the length of the horizon.
