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Dynamic Effects of Credit Shocks in a Data-Rich Environment

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  • Jean Boivin
  • Marc P. 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 reflecting an unexpected deterioration in credit market conditions results in an immediate increase in credit spreads, a decrease in yields of Treasury securities, and causes large and persistent downturns in the activity of many economic sectors. Such shocks are found to have important effects on real activity measures, labor market indicators, aggregate prices, and leading indicators. Our identification procedure which imposes restrictions on the impact response of a small number of economic indicators yields interpretable estimated factors.

Suggested Citation

  • Jean Boivin & Marc P. Giannoni & Dalibor Stevanovic, 2016. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," CIRANO Working Papers 2016s-55, CIRANO.
  • Handle: RePEc:cir:cirwor:2016s-55
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    References listed on IDEAS

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    Cited by:

    1. Stevanovic Dalibor, 2016. "Common time variation of parameters in reduced-form macroeconomic models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 159-183, April.
    2. Pinter, Gabor & Theodoridis, Konstantinos & Yates, Tony, 2013. "Risk news shocks and the business cycle," Bank of England working papers 483, Bank of England.
    3. 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.
    4. Yohei Yamamoto, 2012. "Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions," Global COE Hi-Stat Discussion Paper Series gd12-249, Institute of Economic Research, Hitotsubashi University.
    5. 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.
    6. repec:eee:finlet:v:24:y:2018:i:c:p:186-192 is not listed on IDEAS
    7. 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.
    8. 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.
    9. Marzie Taheri Sanjani, 2014. "Financial Frictions in Data; Evidence and Impact," IMF Working Papers 14/238, International Monetary Fund.
    10. 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.
    11. 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.
    12. 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.
    13. Nathan Bedock & Dalibor Stevanovic, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics, Canadian Economics Association, vol. 50(2), pages 541-570, May.
    14. 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.
    15. 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.
    16. Barnichon, Regis & Matthes, Christian & Ziegenbein, Alexander, 2016. "Theory Ahead of Measurement? Assessing the Nonlinear Effects of Financial Market Disruptions," Working Paper 16-15, Federal Reserve Bank of Richmond.
    17. repec:bla:obuest:v:79:y:2017:i:4:p:546-569 is not listed on IDEAS

    More about this item

    Keywords

    Credit shocks; FAVAR; structural factor analysis;

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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
    • 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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