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bootstrap inference for impulse response functions in factor‐augmented vector autoregressions (replication data)

In this study, we consider residual-based bootstrap methods 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). Both procedures are asymptotically valid under the condition , where N and T are the cross-sectional dimension and the time dimension, respectively. However, Procedure A is also valid even when with 0 ≤ c < ∞ because it accounts for the effect of the factor estimation errors on the impulse response function estimator. Our simulation results suggest that Procedure A achieves more accurate coverage rates than those of Procedure B, especially when N is much smaller than T. In the monetary policy analysis of Bernanke et al. (Quarterly Journal of Economics, 2005, 120(1), 387-422), the proposed methods can produce statistically different results.

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Suggested Citation

Yamamoto, Yohei (2019): Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions (replication data). Version: 1. Journal of Applied Econometrics. Dataset. https://journaldata.zbw.eu/dataset/bootstrap-inference-for-impulse-response-functions-in-factoraugmented-vector-autoregressions?activity_id=7b792421-9aa2-4965-a4e1-7b36a43231dc