IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Dynamic Effects of Credit Shocks in a Data-Rich Environment

  • Jean Boivin
  • Marc P. Giannoni
  • Dalibor Stevanovic

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. The identified credit shocks, interpreted as unexpected deteriorations of credit market conditions, immediately increase credit spreads, decrease rates on Treasury securities, and cause large and persistent downturns in the activity of many economic sectors. Such shocks are found to have important effects on real activity measures, aggregate prices, leading indicators, and credit spreads. Our identification procedure does not require any timing restrictions between the financial and macroeconomic factors, and yields interpretable estimated factors.For an update of this article see: http://cirano.qc.ca/files/publications/2016s-55.pdf

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.cirano.qc.ca/files/publications/2013s-11.pdf
Download Restriction: no

Paper provided by CIRANO in its series CIRANO Working Papers with number 2013s-11.

as
in new window

Length: 1 pages
Date of creation: 01 May 2013
Date of revision:
Handle: RePEc:cir:cirwor:2013s-11
Contact details of provider: Postal:
1130 rue Sherbrooke Ouest, suite 1400, Montréal, Quéc, H3A 2M8

Phone: (514) 985-4000
Fax: (514) 985-4039
Web page: http://www.cirano.qc.ca/
Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
  2. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00638440, HAL.
  3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, 01.
  4. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2014. "Inflation in the Great Recession and New Keynesian Models," NBER Working Papers 20055, National Bureau of Economic Research, Inc.
  5. Alejandro Justiniano & Giorgio E. Primiceri & Andrea Tambalotti, 2009. "Investment shocks and the relative price of investment," Staff Reports 411, Federal Reserve Bank of New York.
  6. Lawrence J. Christiano & Roberto Motto & Massimo Rostagno, 2003. "The Great Depression and the Friedman-Schwartz hypothesis," Proceedings, Federal Reserve Bank of Cleveland, pages 1119-1215.
  7. Jean Boivin & Marc Giannoni, 2006. "DSGE Models in a Data-Rich Environment," NBER Technical Working Papers 0332, National Bureau of Economic Research, Inc.
  8. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
  9. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
  10. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  11. Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
  12. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper Series WP-01-08, Federal Reserve Bank of Chicago.
  13. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
  14. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
  15. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April.
  16. Thomas Helbling & Ayhan Kose & Christopher Otrok & Raju Huidrom, 2010. "Do Credit Shocks Matter? A Global Perspective," IMF Working Papers 10/261, International Monetary Fund.
  17. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
  18. Gary Koop, 2011. "Forecasting with Medium and Large Bayesian VARs," Working Papers 1117, University of Strathclyde Business School, Department of Economics.
  19. Domenico Giannone & Michèle Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," Working Papers ECARES ECARES 2012-002, ULB -- Universite Libre de Bruxelles.
  20. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  21. Lucrezia Reichlin & Domenico Giannone & Luca Sala, . "Monetary policy in real time," ULB Institutional Repository 2013/10177, ULB -- Universite Libre de Bruxelles.
    • Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
  22. Dalibor Stevanovic, 2015. "Factor augmented autoregressive distributed lag models with macroeconomic applications," CIRANO Working Papers 2015s-33, CIRANO.
  23. Gertler, Mark & Lown, Cara S, 1999. "The Information in the High-Yield Bond Spread for the Business Cycle: Evidence and Some Implications," Oxford Review of Economic Policy, Oxford University Press, vol. 15(3), pages 132-50, Autumn.
  24. Jonas D. M. Fisher, 2006. "The Dynamic Effects of Neutral and Investment-Specific Technology Shocks," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 413-451, June.
  25. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
  26. Lawrence Christiano & Roberto Motto & Massimo Rostagno, 2013. "Risk Shocks," NBER Working Papers 18682, National Bureau of Economic Research, Inc.
  27. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
  28. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2007. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Center for Economic Research (RECent) 008, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
  29. Bernanke, Ben & Gertler, Mark, 1995. "Inside the Black Box: The Credit Channel of Monetary Policy Transmission," Working Papers 95-15, C.V. Starr Center for Applied Economics, New York University.
  30. Eickmeier, Sandra & Ng, Tim, 2015. "How do US credit supply shocks propagate internationally? A GVAR approach," European Economic Review, Elsevier, vol. 74(C), pages 128-145.
  31. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, 07.
  32. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
  33. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:cir:cirwor:2013s-11. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Webmaster)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.