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Asymptotically Valid Bootstrap Inference for Proxy SVARs

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  • Carsten Jentsch
  • Kurt G. Lunsford

Abstract

Proxy structural vector autoregressions identify structural shocks in vector autoregressions with external variables that are correlated with the structural shocks of interest but uncorrelated with all other structural shocks. We provide asymptotic theory for this identification approach under mild α-mixing conditions that cover a large class of uncorrelated, but possibly dependent innovation processes. We prove consistency of a residual-based moving block bootstrap (MBB) for inference on statistics such as impulse response functions and forecast error variance decompositions. The MBB serves as the basis for constructing confidence intervals when the proxy variables are strongly correlated with the structural shocks of interest. For the case of one proxy variable used to identify one structural shock, we show that the MBB can be used to construct confidence sets for normalized impulse responses that are valid regardless of proxy strength based on the inversion of the Anderson and Rubin statistic suggested by Montiel Olea, Stock, and Watson.

Suggested Citation

  • Carsten Jentsch & Kurt G. Lunsford, 2022. "Asymptotically Valid Bootstrap Inference for Proxy SVARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1876-1891, October.
  • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:4:p:1876-1891
    DOI: 10.1080/07350015.2021.1990770
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    Cited by:

    1. Giovanni Angelini & Luca Fanelli, 2019. "Exogenous uncertainty and the identification of structural vector autoregressions with external instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 951-971, September.
    2. Karamysheva, Madina, 2022. "How do fiscal adjustments work? An empirical investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    3. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2020. "Uncertainty and monetary policy in good and bad times: A Replication of the VAR investigation by Bloom (2009)," CAMA Working Papers 2020-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Giovanni Angelini & Giovanni Caggiano & Efrem Castelnuovo & Luca Fanelli, 2023. "Are Fiscal Multipliers Estimated with Proxy‐SVARs Robust?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(1), pages 95-122, February.
    5. repec:zbw:bofrdp:2020_013 is not listed on IDEAS
    6. Härtl, Tilmann, 2022. "Identifying Proxy VARs with Restrictions on the Forecast Error Variance," VfS Annual Conference 2022 (Basel): Big Data in Economics 264071, Verein für Socialpolitik / German Economic Association.
    7. Cesa-Bianchi, Ambrogio & Thwaites, Gregory & Vicondoa, Alejandro, 2020. "Monetary policy transmission in the United Kingdom: A high frequency identification approach," European Economic Review, Elsevier, vol. 123(C).
    8. Fengler, Matthias & Polivka, Jeannine, 2021. "Identifying structural shocks to volatility through a proxy-MGARCH model," Economics Working Paper Series 2103, University of St. Gallen, School of Economics and Political Science, revised May 2021.
    9. Montiel Olea, José L. & Stock, James H. & Watson, Mark W., 2021. "Inference in Structural Vector Autoregressions identified with an external instrument," Journal of Econometrics, Elsevier, vol. 225(1), pages 74-87.
    10. Maghyereh, Aktham & Abdoh, Hussein, 2021. "The effect of structural oil shocks on bank systemic risk in the GCC countries," Energy Economics, Elsevier, vol. 103(C).
    11. Giovanni Angelini & Giovanni Caggiano & Efrem Castelnuovo & Luca Fanelli, 2020. "Are Fiscal Multipliers Estimated with Proxy-SVARs Robust?," "Marco Fanno" Working Papers 0257, Dipartimento di Scienze Economiche "Marco Fanno".

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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