局部投影推断比你想象的更简单且更稳健
Local Projection Inference is Simpler and More Robust Than You Think
AI总结：
本文证明局部投影推断在处理持久数据和远期冲击响应时比标准自回归推断更简单且更稳健。
摘要：
应用宏观经济学家常通过局部投影计算冲击响应的置信区间，即对未来的结果直接进行当前协变量的线性回归。本文证明局部投影推断能够稳健地处理应用中常见的两个问题：高度持久的数据和远期冲击响应的估计。我们考虑在回归中控制变量滞后项的局部投影。我们展示滞后增强的局部投影在使用正常临界值时，对（i）平稳和非平稳数据以及（ii）广泛响应期限都是渐近有效的。此外，滞后增强消除了对回归残差中自相关修正标准误差的必要性。因此，局部投影推断可能比以前认为的更简单，并且比标准自回归推断更稳健，后者的有效性已知对数据的持续性和期限长度敏感。
英文摘要：
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.
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