Local Projection Inference Is Simpler and More Robust Than You Think
Local Projection Inference Is Simpler and More Robust Than You Think
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DOI：
10.3982/ecta18756
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发表时间：
2020-07
期刊：
影响因子：
6.1
通讯作者：
J. L. Montiel;Marco del Negro;D. Giannone;Michael J. Kolesar;Simon Lee;Konrad Menzel;Ulrich K. Müller;Serena Ng;Christian K. Wolf
中科院分区：
文献类型：
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作者：
J. L. Montiel;Marco del Negro;D. Giannone;Michael J. Kolesar;Simon Lee;Konrad Menzel;Ulrich K. Müller;Serena Ng;Christian K. Wolf
应用宏观经济学家经常使用局部预测来计算脉冲响应的置信区间，即对当前协变量的未来结果进行直接线性回归。本文证明了局部投影推理鲁棒地处理了应用中经常出现的两个问题：高度持久的数据和长时间范围内的脉冲响应估计。我们考虑控制回归中变量滞后的局部预测。我们证明了具有正常临界值的滞后增广局部投影在（i）平稳和非平稳数据上以及（ii）广泛的响应范围内一致渐近有效。此外，滞后增强消除了需要在回归残差中校正序列相关性的标准误差。因此，局部投影推断可以说比以前认为的更简单，比标准的自回归推断更鲁棒，其有效性已知敏感地依赖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, that is, 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.