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Credit Shocks and Cycles: a Bayesian Calibration Approach

Author

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  • Roland Meeks

    (Nuffield College, University of Oxford)

Abstract

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.

Suggested Citation

  • Roland Meeks, 2006. "Credit Shocks and Cycles: a Bayesian Calibration Approach," Economics Papers 2006-W11, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:0611
    as

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    File URL: http://www.nuffield.ox.ac.uk/economics/papers/2006/w11/meeks_creditWP.pdf
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    References listed on IDEAS

    as
    1. 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 and Oxford Review of Economic Policy Limited, vol. 15(3), pages 132-150, Autumn.
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    8. 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 402-417, June.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
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    Cited by:

    1. Kévin Beaubrun-Diant & Fabien Tripier, 2009. "The Credit Spread Cycle with Matching Friction," Working Papers hal-00430809, HAL.

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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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