Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions
研究了因子增强向量自回归模型中结构脉冲响应函数的自助法置信区间构建，比较了两种自助法程序，发现考虑因子估计误差的程序在N远小于T时覆盖率更准确，并在货币政策分析中产生显著不同结果。
Summary 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 , where N and T are 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 when N is much smaller than T . 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.