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

## Abstract:

In this paper, we consider residual-based bootstrap methods à la Conçalves and Perron (2014) to construct the confidence interval for structural impulse response functions in factor-augmented vector autoregression. In particular, we compare the bootstrap with 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.

Report No.: | HIAS-E-26 |
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Author(s): | Yohei Yamamoto |

Affiliation: | Department of Economics, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601 Japan |

Issued Date: | May 2016 |

Keywords: | factor-augmented vector autoregression, structural identification, coverage rate, impulse response function |

JEL: | C14, C22 |

Links: | PDF, HERMES-IR, RePEc |