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Instrumental Variable Identification of Dynamic Variance Decompositions


  • Mikkel Plagborg-Møller
  • Christian K. Wolf


Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving-average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various additional restrictions guarantee point identification of both variance and historical decompositions. Unlike structural vector autoregression analysis, our methods do not require invertibility. Applied to US data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.

Suggested Citation

  • Mikkel Plagborg-Møller & Christian K. Wolf, 2022. "Instrumental Variable Identification of Dynamic Variance Decompositions," Journal of Political Economy, University of Chicago Press, vol. 130(8), pages 2164-2202.
  • Handle: RePEc:ucp:jpolec:doi:10.1086/720141
    DOI: 10.1086/720141

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    Cited by:

    1. Gorodnichenko, Yuriy & Lee, Byoungchan, 2017. "A Note on Variance Decomposition with Local Projections," Department of Economics, Working Paper Series qt8878h9r2, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    2. Bruns, Martin & Lütkepohl, Helmut, 2022. "Comparison of local projection estimators for proxy vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    4. Xavier Gabaix & Ralph S. J. Koijen, 2020. "Granular Instrumental Variables," Working Papers 2020-177, Becker Friedman Institute for Research In Economics.
    5. Helmut Herwartz & Christian Ochsner & Hannes Rohloff, 2021. "Global Credit Shocks and Real Economies," MAGKS Papers on Economics 202116, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    6. Dias, Daniel A. & Duarte, João B., 2015. "Monetary Policy and Homeownership: Empirical Evidence, Theory, and Policy Implications," MPRA Paper 112252, University Library of Munich, Germany, revised 05 Mar 2021.
    7. Fabio Canova & Filippo Ferroni, 2022. "Mind the Gap! Stylized Dynamic Facts and Structural Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 104-135, October.
    8. Danilo Cascaldi-Garcia, 2022. "Forecast Revisions as Instruments for News Shocks," International Finance Discussion Papers 1341, Board of Governors of the Federal Reserve System (U.S.).
    9. Andrea Gazzani & Alejandro Vicondoa, 2020. "Bridge Proxy-SVAR: estimating the macroeconomic effects of shocks identified at high-frequency," Temi di discussione (Economic working papers) 1274, Bank of Italy, Economic Research and International Relations Area.
    10. Antoine Levy & Mr. Luca A Ricci & Alejandro M. Werner, 2020. "The Sources of Fiscal Fluctuations," IMF Working Papers 2020/220, International Monetary Fund.
    11. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    12. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    13. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The real effects of financial uncertainty shocks: A daily identification approach," Working Papers 61, Red Nacional de Investigadores en Economía (RedNIE).
    14. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.

    More about this item

    JEL classification:

    • 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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation


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