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Monetary policy and information shocks in a block-recursive SVAR

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  • Keweloh, Sascha A.
  • Hetzenecker, Stephan
  • Seepe, Andre

Abstract

This study introduces a new estimator that combines block-recursive restrictions with higher-order moment conditions and non-Gaussian shocks. The proposed estimator improves the accuracy of the estimation, simplifies labeling, and allows for relaxing the independence and non-Gaussianity assumptions in comparison to a purely data-driven approach. We use the approach to disentangle the interaction of stock prices and interest rates into monetary policy and stock market information shocks. We find that traditional monetary policy shocks move interest rates and stock prices in opposite directions, whereas information shocks move both variables in the same direction. Moreover, we utilize high-frequency data from FOMC announcements to derive a proxy for central bank information shocks and show that these shocks are statistically relevant for the low-frequency stock market information shock.

Suggested Citation

  • Keweloh, Sascha A. & Hetzenecker, Stephan & Seepe, Andre, 2023. "Monetary policy and information shocks in a block-recursive SVAR," Journal of International Money and Finance, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:jimfin:v:137:y:2023:i:c:s0261560623000931
    DOI: 10.1016/j.jimonfin.2023.102892
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    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    3. Gouriéroux, Christian & Monfort, Alain & Renne, Jean-Paul, 2017. "Statistical inference for independent component analysis: Application to structural VAR models," Journal of Econometrics, Elsevier, vol. 196(1), pages 111-126.
    4. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
    5. Refet Gürkaynak & Hati̇ce Gökçe Karasoy‐Can & Sang Seok Lee, 2022. "Stock Market's Assessment of Monetary Policy Transmission: The Cash Flow Effect," Journal of Finance, American Finance Association, vol. 77(4), pages 2375-2421, August.
    6. 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.
    7. José Luis Montiel Olea & Mikkel Plagborg-Møller & Eric Qian, 2022. "SVAR Identification from Higher Moments: Has the Simultaneous Causality Problem Been Solved?," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 481-485, May.
    8. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    9. Marek Jarociński & Peter Karadi, 2020. "Deconstructing Monetary Policy Surprises—The Role of Information Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(2), pages 1-43, April.
    10. Sascha Alexander Keweloh, 2021. "A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 772-782, July.
    11. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    12. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    13. Daniel J Lewis, 2021. "Identifying Shocks via Time-Varying Volatility [First Order Autoregressive Processes and Strong Mixing]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 3086-3124.
    14. Bjørnland, Hilde C. & Leitemo, Kai, 2009. "Identifying the interdependence between US monetary policy and the stock market," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 275-282, March.
    15. David S. Matteson & Ruey S. Tsay, 2017. "Independent Component Analysis via Distance Covariance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 623-637, April.
    16. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with smooth transition in variances," Journal of Economic Dynamics and Control, Elsevier, vol. 84(C), pages 43-57.
    17. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    18. Michael D. Bauer & Eric T. Swanson, 2023. "An Alternative Explanation for the "Fed Information Effect"," American Economic Review, American Economic Association, vol. 113(3), pages 664-700, March.
    19. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    20. Marek Jarocinski & Peter Karadi, 2017. "Central Bank Information Shocks," 2017 Meeting Papers 1193, Society for Economic Dynamics.
    21. Helmut Herwartz & Simone Maxand & Hannes Rohloff, 2022. "The Link between Monetary Policy, Stock Prices, and House Prices—Evidence from a Statistical Identification Approach," International Journal of Central Banking, International Journal of Central Banking, vol. 18(5), pages 1-53, December.
    22. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    23. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    24. Markku Lanne & Jani Luoto, 2021. "GMM Estimation of Non-Gaussian Structural Vector Autoregression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 69-81, January.
    25. Herwartz, Helmut & Plödt, Martin, 2016. "The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 30-44.
    26. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    27. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
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    Cited by:

    1. Sascha A. Keweloh, 2023. "Structural Vector Autoregressions and Higher Moments: Challenges and Solutions in Small Samples," Papers 2310.08173, arXiv.org.

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