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The puzzling effects of monetary policy in VARs: Invalid identification or missing information?

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  • Mark Kerssenfischer

Abstract

Standard vector autoregressions (VARs) often find puzzling effects of monetary policy shocks. Is this due to an invalid (recursive) identification scheme, or because the underlying small‐scale VAR neglects important information? I employ factor methods and external instruments to answer this question and provide evidence that the root cause is missing information. In particular, while a recursively identified dynamic factor model yields conventional monetary policy effects across the board, a small‐scale VAR identified via external instruments does not. Importantly, the discrepancy between both models largely disappears once the information set of the VAR is augmented via factors. This finding is comforting news for the recent monetary literature. Two leading empirical advances with different underlying assumptions—namely external instruments (applied to a factor‐augmented VAR) and dynamic factor models (identified recursively)—find very similar effects of monetary policy shocks, cross‐verifying each other.

Suggested Citation

  • Mark Kerssenfischer, 2019. "The puzzling effects of monetary policy in VARs: Invalid identification or missing information?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 18-25, January.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:1:p:18-25
    DOI: 10.1002/jae.2647
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    Citations

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    Cited by:

    1. Yang, Yang & Zhang, Jiqiang, 2021. "Effects of monetary policy on the exchange rates: A Time-varying analysis," Finance Research Letters, Elsevier, vol. 43(C).
    2. Paul Hubert & Frédérique Savignac, 2023. "Monetary Policy and Labor Income Inequality: the Role of Extensive and Intensive Margins," Working papers 913, Banque de France.
    3. Kerssenfischer, Mark, 2019. "Information Effects of Euro Area Monetary Policy: New evidence from high-frequency futures data," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203524, Verein für Socialpolitik / German Economic Association.
    4. 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.
    5. Giorgia De Nora, 2021. "Factor Augmented Vector-Autoregression with narrative identification. An application to monetary policy in the US," Working Papers 934, Queen Mary University of London, School of Economics and Finance.
    6. Martin Bruns, 2019. "Proxy VAR models in a data-rich environment," University of East Anglia School of Economics Working Paper Series 2019-03, School of Economics, University of East Anglia, Norwich, UK..
    7. 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.
    8. Maurizio Daniele & Julie Schnaitmann, 2019. "A Regularized Factor-augmented Vector Autoregressive Model," Papers 1912.06049, arXiv.org.
    9. Harald Badinger & Stefan Schiman, 2020. "Measuring Monetary Policy with Residual Sign Restrictions at Known Shock Dates," Department of Economics Working Papers wuwp300, Vienna University of Economics and Business, Department of Economics.
    10. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    11. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).

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