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Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession

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  • Martin Feldkircher
  • Florian Huber
  • Michael Pfarrhofer

Abstract

The COVID-19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks engaged in expansionary monetary policy. This paper asks whether the measures adopted by the US Federal Reserve (Fed) have been effective in boosting real activity and calming financial markets. To measure these effects at high frequencies, we propose a novel mixed frequency vector autoregressive (MF-VAR) model. This model allows us to combine weekly and monthly information within an unified framework. Our model combines a set of macroeconomic aggregates such as industrial production, unemployment rates and inflation with high frequency information from financial markets such as stock prices, interest rate spreads and weekly information on the Feds balance sheet size. The latter set of high frequency time series is used to dynamically interpolate the monthly time series to obtain weekly macroeconomic measures. We use this setup to simulate counterfactuals in absence of monetary stimulus. The results show that the monetary expansion caused higher output growth and stock market returns, more favorable long-term financing conditions and a depreciation of the US dollar compared to a no-policy benchmark scenario.

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  • Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2020. "Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession," Papers 2007.15419, arXiv.org.
  • Handle: RePEc:arx:papers:2007.15419
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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Andreas Schrimpf & Hyun Song Shin & Vladyslav Sushko, 2020. "Leverage and margin spirals in fixed income markets during the Covid-19 crisis," BIS Bulletins 2, Bank for International Settlements.
    3. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    4. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    5. Joseph Gagnon & Matthew Raskin & Julie Remache & Brian Sack, 2011. "The Financial Market Effects of the Federal Reserve's Large-Scale Asset Purchases," International Journal of Central Banking, International Journal of Central Banking, vol. 7(1), pages 3-43, March.
    6. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
    7. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.
    8. Hess Chung & Jean‐Philippe Laforte & David Reifschneider & John C. Williams, 2012. "Have We Underestimated the Likelihood and Severity of Zero Lower Bound Events?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(s1), pages 47-82, February.
    9. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    10. Michael Joyce & David Miles & Andrew Scott & Dimitri Vayanos, 2012. "Quantitative Easing and Unconventional Monetary Policy – an Introduction," Economic Journal, Royal Economic Society, vol. 122(564), pages 271-288, November.
    11. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    13. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    14. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    15. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2020. "Computationally efficient inference in large Bayesian mixed frequency VARs," Economics Letters, Elsevier, vol. 191(C).
    16. Jacopo Cimadomo & Antonello D'Agostino, 2016. "Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1276-1290, November.
    17. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    18. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    19. Yavuz Arslan & Mathias Drehmann & Boris Hofmann, 2020. "Central bank bond purchases in emerging market economies," BIS Bulletins 20, Bank for International Settlements.
    20. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    21. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    22. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed‐Frequency Structural Models: Identification, Estimation, And Policy Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1118-1144, November.
    23. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    24. Efrem Castelnuovo & Paolo Surico, 2010. "Monetary Policy, Inflation Expectations and The Price Puzzle," Economic Journal, Royal Economic Society, vol. 120(549), pages 1262-1283, December.
    25. Alessandro Rebucci & Jonathan S. Hartley & Daniel Jiménez, 2022. "An Event Study of COVID-19 Central Bank Quantitative Easing in Advanced and Emerging Economies," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 291-322, Emerald Group Publishing Limited.
    26. Joyce, Michael, 2012. "Quantitative easing and other unconventional monetary policies: Bank of England conference summary," Bank of England Quarterly Bulletin, Bank of England, vol. 52(1), pages 48-56.
    27. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
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    2. Mariana Hatmanu & Cristina Cautisanu, 2021. "The Impact of COVID-19 Pandemic on Stock Market: Evidence from Romania," IJERPH, MDPI, vol. 18(17), pages 1-22, September.
    3. Ayhan Kuloğlu, 2021. "Covıd-19 Krizinin Petrol Fiyatları Üzerine Etkisi," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 6(3), pages 710-727.
    4. Jacek Pietrucha, 2021. "Drivers of the Cash Paradox," Risks, MDPI, vol. 9(12), pages 1-17, December.
    5. Yilmazkuday, Hakan, 2022. "COVID-19 and Monetary policy with zero bounds: A cross-country investigation," Finance Research Letters, Elsevier, vol. 44(C).
    6. Víctor Manuel Cuevas Ahumada & Cuauhtémoc Calderón Villarreal, 2023. "Government policies and manufacturing production during the COVID-19 pandemic," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(4), pages 1-19, Octubre -.
    7. Müller, Fernanda Maria & Santos, Samuel Solgon & Righi, Marcelo Brutti, 2023. "A description of the COVID-19 outbreak role in financial risk forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    8. Andre Amaral & Taysir E. Dyhoum & Hussein A. Abdou & Hassan M. Aljohani, 2022. "Modeling for the Relationship between Monetary Policy and GDP in the USA Using Statistical Methods," Mathematics, MDPI, vol. 10(21), pages 1-20, November.

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