Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions
No HIAS-E-26, Discussion paper series from Hitotsubashi Institute for Advanced Study, Hitotsubashi University
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.
Keywords: factor-augmented vector autoregression; structural identiOcation; coverage rate; impulse response function (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions (2019)
Working Paper: Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:hit:hiasdp:hias-e-26
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