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

Yohei Yamamoto

Global COE Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University

Abstract: This paper investigates structural identification and residual-based bootstrap inference schemes for impulse response functions (IRFs) in factor-augmented vector autoregressions (FAVARs). I first discuss general conditions for structural identification, which also resolve the random rotation of the principal components estimates. I also provide empirically popular three such identification schemes: short-run, long-run and contemporaneous restrictions with sign restrictions. Second, two bootstrap procedures for the identified structural IRFs are compared: A) bootstrap with factor estimation and B) bootstrap without factor estimation. Although both procedures are asymptotically valid in the first-order under √T/N→0 (T and N are the time and the cross sectional dimensions), the errors in the factor estimation produce higher-order discrepancies. The asymptotic normal intervals also tend to provide smaller coverage ratios and are quite erratic. Monte Carlo simulations and an empirical example confirm the theoretical findings.

Keywords: structural identification; principal components; factor rotation; coverage ratios; factor estimation errors (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2012-10
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Citations: View citations in EconPapers (12)

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http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd12-249.pdf (application/pdf)

Related works:
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 (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:hst:ghsdps:gd12-249

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