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Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions

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  • Yohei Yamamoto

Abstract

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 T/N→0, where N and T are the cross‐sectional dimension and the time dimension, respectively. However, Procedure A is also valid even when T/N→c with 0 ≤ c

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  • Yohei Yamamoto, 2019. "Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 247-267, March.
  • Handle: RePEc:wly:japmet:v:34:y:2019:i:2:p:247-267
    DOI: 10.1002/jae.2659
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    Cited by:

    1. Bicu A.C. & Lieb L.M., 2015. "Cross-border effects of fiscal policy in the Eurozone," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
    2. Mototsugu Shintani & Zi-Yi Guo, 2018. "Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 360-379, April.
    3. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, Open Access Journal, vol. 7(3), pages 1-43, July.
    5. Herrera, Ana María & Rangaraju, Sandeep Kumar, 2019. "The quantitative effects of tax foresight: Not all states are equal," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    6. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2017. "Structural Factor Analysis of Interest Rate Pass Through In Four Large Euro Area Economies," Working Papers in Economics 17/07, University of Canterbury, Department of Economics and Finance.
    7. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
    8. Martin Bruns, 2019. "Proxy VAR Models in a Data-Rich Environment," Discussion Papers of DIW Berlin 1831, DIW Berlin, German Institute for Economic Research.
    9. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
    10. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    11. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    12. Shintani, Mototsugu & Guo, Zi-Yi, 2011. "Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations," EconStor Preprints 167627, ZBW - Leibniz Information Centre for Economics.
    13. Herrera, Ana María & Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2017. "Where do jobs go when oil prices drop?," Energy Economics, Elsevier, vol. 64(C), pages 469-482.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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