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Business Cycle Effects of Credit and Technology Shocks in a DSGE Model with Firm Defaults

  • M. Hashem Pesaran
  • TengTeng Xu

This paper proposes a theoretical framework to analyze the impacts of credit and technology shocks on business cycle dynamics, where firms rely on banks and households for capital financing. Firms are identical ex ante but differ ex post due to different realizations of firm specific technology shocks, possibly leading to default by some firms. The paper advances a new modelling approach for the analysis of financial intermediation and firm defaults that takes account of the financial implications of such defaults for both households and banks. Results from a calibrated version of the model highlights the role of financial institutions in the transmission of credit and technology shocks to the real economy. A positive credit shock, defined as a rise in the loan to deposit ratio, increases output, consumption, hours and productivity, and reduces the spread between loan and deposit rates. The effects of the credit shock tend to be highly persistent even without price rigidities and habit persistence in consumption behaviour.

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Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 3609.

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Date of creation: 2011
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Handle: RePEc:ces:ceswps:_3609
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  1. 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.
  2. Ibrahim Chowdhury & Mathias Hoffmann & Andreas Schabert, 2004. "Inflation Dynamics and the Cost Channel of Monetary Transmission," Money Macro and Finance (MMF) Research Group Conference 2004 18, Money Macro and Finance Research Group.
  3. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
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  7. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
  8. Binder, Michael & Pesaran, M. Hashem, 1997. "Multivariate Linear Rational Expectations Models," Econometric Theory, Cambridge University Press, vol. 13(06), pages 877-888, December.
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  12. Alexius, Annika & Carlsson, Mikael, 2007. "Production function residuals, VAR technology shocks, and hours worked: Evidence from industry data," Economics Letters, Elsevier, vol. 96(2), pages 259-263, August.
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  16. Binder,M. & Pesaran,H.M., 1995. "Multivariate Rational Expectations Models and Macroeconomic Modelling: A Review and Some New Results," Cambridge Working Papers in Economics 9415, Faculty of Economics, University of Cambridge.
  17. Fabio Canova & David Lopez-Salido & Claudio Michelacci, 2010. "The effects of technology shocks on hours and output: a robustness analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 755-773.
  18. Ravenna, Federico & Walsh, Carl E., 2006. "Optimal monetary policy with the cost channel," Journal of Monetary Economics, Elsevier, vol. 53(2), pages 199-216, March.
  19. Christiano, Lawrence & Ilut, Cosmin & Motto, Roberto & Rostagno, Massimo, 2008. "Monetary policy and stock market boom-bust cycles," Working Paper Series 0955, European Central Bank.
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