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Credit Shocks and Cycles: a Bayesian Calibration Approach

  • Roland Meeks


    (Nuffield College, University of Oxford)

This paper asks how well a general equilibrium agency cost model describes the dynamic relationship between credit variables and the business cycle. A Bayesian VAR is used to obtain probability intervals for empirical correlations. The agency cost model is found to predict the leading, countercyclical correlation of spreads with output when shocks arising from the credit market contribute to output fluctuations. The contribution of technology shocks is held at conventional RBC levels. Sensitivity analysis shows that moderate prior calibration uncertainty leads to significant dispersion in predictedcorrelations. Most predictive uncertainty arises from a single parameter.

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Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2006-W11.

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Length: 29 pages
Date of creation: 25 Aug 2006
Date of revision:
Handle: RePEc:nuf:econwp:0611
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  1. Ireland, Peter N., 2003. "Endogenous money or sticky prices?," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1623-1648, November.
  2. Li, Wenli & Sarte, Pierre-Daniel G., 2003. "Credit market frictions and their direct effects on U.S. manufacturing fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 28(3), pages 419-443, December.
  3. L. Wade, 1988. "Review," Public Choice, Springer, vol. 58(1), pages 99-100, July.
  4. Greenwood, Jeremy & Hercowitz, Zvi & Huffman, Gregory W, 1988. "Investment, Capacity Utilization, and the Real Business Cycle," American Economic Review, American Economic Association, vol. 78(3), pages 402-17, June.
  5. Bernanke, Ben & Gertler, Mark, 1989. "Agency Costs, Net Worth, and Business Fluctuations," American Economic Review, American Economic Association, vol. 79(1), pages 14-31, March.
  6. DeJong, David N & Ingram, Beth Fisher & Whiteman, Charles H, 1996. "A Bayesian Approach to Calibration," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 1-9, January.
  7. Cooper, Russell & Ejarque, Jo o, 2000. "Financial Intermediation And Aggregate Fluctuations: A Quantitative Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 4(04), pages 423-447, December.
  8. Siem Jan Koopman & Andr� Lucas, 2003. "Business and Default Cycles for Credit Risk," Tinbergen Institute Discussion Papers 03-062/2, Tinbergen Institute, revised 09 Jan 2003.
  9. Canova, Fabio, 1995. "Sensitivity Analysis and Model Evaluation in Simulated Dynamic General Equilibrium Economies," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(2), pages 477-501, May.
  10. Kwark, Noh-Sun, 2002. "Default risks, interest rate spreads, and business cycles: Explaining the interest rate spread as a leading indicator," Journal of Economic Dynamics and Control, Elsevier, vol. 26(2), pages 271-302, February.
  11. 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.
  12. Russell Cooper & Joao Ejarque, 1994. "Financial Intermediation and Aggregate Fluctuations: A Quantative Analysis," NBER Working Papers 4819, National Bureau of Economic Research, Inc.
  13. David N. DeJong & Beth F. Ingram & Charles H. Whiteman, 2000. "Keynesian impulses versus Solow residuals: identifying sources of business cycle fluctuations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(3), pages 311-329.
  14. repec:cup:macdyn:v:4:y:2000:i:4:p:423-47 is not listed on IDEAS
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