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Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions

Listed author(s):
  • YAMAMOTO, Yohei

In this paper, we consider residual-based bootstrap methods à la GonÇalves and Perron (2014) 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). In theory, both procedures are asymptotically valid under a condition √T/N → 0, where N and T are the cross-sectional 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.

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File URL: http://hermes-ir.lib.hit-u.ac.jp/rs/bitstream/10086/27924/1/070_hiasDP-E-26.pdf
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Paper provided by Hitotsubashi Institute for Advanced Study, Hitotsubashi University in its series Discussion paper series with number HIAS-E-26.

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Length: 39 p.
Date of creation: 28 May 2016
Handle: RePEc:hit:hiasdp:hias-e-26
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