
Bootstrap Inference for Impulse Response Functions in FactorAugmented Vector AutoregressionsBootstrap Inference for Impulse Response Functions in FactorAugmented Vector Autoregressions 
"/YAMAMOTO, Yohei/"YAMAMOTO, Yohei
20160528 , Hitotsubashi Institute for Advanced Study, Hitotsubashi University
Description
In this paper, we consider residualbased bootstrap methods à la GonÇalves and Perron (2014) to construct the confidence interval for structural impulse response functions in factoraugmented vector autoregressions. In particular, we compare the bootstrap with factor estimation (Procedure A) with the bootstrap without factor estimation (Procedure B). In theory, both procedures are asymptotically valid under a condition √T/N → 0, where N and T are the crosssectional dimension and the time dimension, respectively. Even when √T/N → 0 is irrelevant, Procedure A still accounts for the effect of the factor estimation errors on the impulse response function estimate and it achieves good coverage rates in most cases. On the contrary, Procedure B is invalid in such cases and tends to undercover if N is much smaller than T. However, Procedure B is implemented more straightforwardly from the standard structural VARs and the length of the confidence interval is shorter than that of Procedure A in finite samples. Given that Procedure B still gives a satisfactory coverage rate unless N is very small, it remains in consideration of empirical use, although using Procedure A is safer as it correctly accounts for the effect of the factor estimation errors.
FullText
http://hermesir.lib.hitu.ac.jp/rs/bitstream/10086/27924/1/070_hiasDPE26.pdf