Bootstrap Inference for Impulse Response Functions in Factor‐Augmented Vector Autoregressions
Bootstrap Inference for Impulse Response Functions in Factor‐Augmented Vector Autoregressions
复制标题
因子增强向量自回归中脉冲响应函数的自举推理
DOI：
10.1002/jae.2659
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发表时间：
2019
影响因子：
2.1
通讯作者：
Yohei Yamamoto
中科院分区：
文献类型：
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作者：
板橋拓己;妹尾哲志;飯田洋介;北村厚;河合信晴;葛谷彩;Tomoya Mori and Jens Wrona;久松太郎;「倒産と担保・保証」実務研究会（和田勝行）;Yohei Yamamoto
在本研究中，我们考虑使用基于残差的引导方法来构建因子增强向量自回归中结构脉冲响应函数的置信区间。特别是，我们将带因子估计的引导程序（程序 A）与不带因子估计的引导程序（程序 B）进行比较。两个过程在 条件下渐近有效，其中 N 和 Ta 分别是横截面维度和时间维度。然而，即使当 0 ≤c<∞ 时，过程 A 也是有效的，因为它考虑了因子估计误差对脉冲响应函数估计器的影响。我们的模拟结果表明，程序 A 比程序 B 实现了更准确的覆盖率，特别是当 N 远小于 T 时。在伯南克等人的货币政策分析中。 (Quarterly Journal of Economics, 2005,120(1), 387–422)，所提出的方法可以产生统计上不同的结果。
In this study, we consider residual‐based bootstrap methods to construct the confidence interval for structural impulse response functions in factor‐augmented vector autoregressions. In particular, we compare the bootstrap with factor estimation (Procedure A) with the bootstrap without factor estimation (Procedure B). Both procedures are asymptotically valid under the condition , whereNandTare the cross‐sectional dimension and the time dimension, respectively. However, Procedure A is also valid even when with 0 ≤c<∞because it accounts for the effect of the factor estimation errors on the impulse response function estimator. Our simulation results suggest that Procedure A achieves more accurate coverage rates than those of Procedure B, especially whenNis much smaller thanT. In the monetary policy analysis of Bernanke et al. (Quarterly Journal of Economics, 2005,120(1), 387–422), the proposed methods can produce statistically different results.