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Dynamic effects of credit shocks in a data-rich environment

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  • Jean Boivin
  • Marc Giannoni
  • Dalibor Stevanovic

Abstract

We examine the dynamic effects of credit shocks using a large data set of U.S. economic and financial indicators in a structural factor model. An identified credit shock resulting in an unanticipated increase in credit spreads causes a large and persistent downturn in indicators of real economic activity, labor market conditions, expectations of future economic conditions, a gradual decline in aggregate price indices, and a decrease in short- and longer-term riskless interest rates. Our identification procedure, which imposes restrictions on the response of a small number of economic indicators, yields interpretable estimated factors, and allows us to perform counterfactual experiments. Such an experiment suggests that credit spread shocks have largely contributed to the deterioration in economic conditions during the Great Recession.

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  • Jean Boivin & Marc Giannoni & Dalibor Stevanovic, 2013. "Dynamic effects of credit shocks in a data-rich environment," Staff Reports 615, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:615
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Econometrics > Time Series Models > Dynamic Factor Models > Structural Factor Models

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    Cited by:

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    2. Caldara, Dario & Fuentes-Albero, Cristina & Gilchrist, Simon & Zakrajšek, Egon, 2016. "The macroeconomic impact of financial and uncertainty shocks," European Economic Review, Elsevier, vol. 88(C), pages 185-207.
    3. Simon Beyeler & Sylvia Kaufmann, 2016. "Factor augmented VAR revisited - A sparse dynamic factor model approach," Working Papers 16.08, Swiss National Bank, Study Center Gerzensee.
    4. Popp, Aaron & Zhang, Fang, 2016. "The macroeconomic effects of uncertainty shocks: The role of the financial channel," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 319-349.
    5. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2012. "Bank Leverage Shocks and the Macroeconomy: a New Look in a Data-Rich Environment," CIRANO Working Papers 2012s-23, CIRANO.
    6. Banerjee, Ryan & Devereux, Michael B. & Lombardo, Giovanni, 2016. "Self-oriented monetary policy, global financial markets and excess volatility of international capital flows," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 275-297.
    7. Nathan Bedock & Dalibor Stevanović, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(2), pages 541-570, May.
    8. Barnichon, Régis & Matthes, Christian & Ziegenbein, Alexander, 2016. "Assessing the Non-Linear Effects of Credit Market Shocks," CEPR Discussion Papers 11410, C.E.P.R. Discussion Papers.
    9. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
    10. Covas, Francisco B. & Rump, Ben & Zakrajšek, Egon, 2014. "Stress-testing US bank holding companies: A dynamic panel quantile regression approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 691-713.
    11. Yohei Yamamoto, 2019. "Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 247-267, March.
    12. Valls Pereira, Pedro L. & da Silva Fonseca, Marcelo Gonçalves, 2012. "Credit Shocks and Monetary Policy in Brazil: A Structural Favar Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 32(2), April.
    13. Marzie Taheri Sanjani, 2014. "Financial Frictions in Data; Evidence and Impact," IMF Working Papers 2014/238, International Monetary Fund.
    14. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: lessons from the financial crisis," Working Paper Series 2468, European Central Bank.
    15. Regis Barnichon & Christian Matthes & Alexander Ziegenbein, 2016. "Theory Ahead of Measurement? Assessing the Nonlinear Effects of Financial Market Disruptions," Working Paper 16-15, Federal Reserve Bank of Richmond.
    16. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2017. "The Macroeconomic Effects of Shocks to Large Banks’ Capital," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 546-569, August.
    17. Österholm, Pär, 2018. "The relation between treasury yields and corporate bond yield spreads in Australia: Evidence from VARs," Finance Research Letters, Elsevier, vol. 24(C), pages 186-192.
    18. Mao Takongmo, Charles Olivier & Stevanovic, Dalibor, 2015. "Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 177-233, Mars-Juin.
    19. Pinter, Gabor & Theodoridis, Konstantinos & Yates, Tony, 2013. "Risk news shocks and the business cycle," Bank of England working papers 483, Bank of England.

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    More about this item

    Keywords

    credit shocks; FAVAR; structural factor analysis;
    All these keywords.

    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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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