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Investigating asymmetries in the bank lending channel. An analysis using Austrian banks’ balance sheet data

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Author Info

  • Sylvia Fruehwirth-Schnatter

    ()
    (Johannes Kepler Universitaet Linz, Department of Applied Statistics and Econometrics)

  • Sylvia Kaufmann

    ()
    (Oesterreichische Nationalbank, Economic Studies Division)

Abstract

In the present paper we use a balanced bank panel data set to obtain an inference on two dimensions of the asymmetric response of bank lending to interest rate changes. The cross-sectional dimension is captured by group-specific parameters whereby each bank’s group membership is estimated along with the model parameters. Moreover, the asymmetric response over time is modelled with switching parameters that depend on a latent state variable. The presence of two latent indicators calls for Bayesian simulation methods. The results show that three bank groups, characterized by the groups' average asset total, differ in their lending reaction to interest rate changes. Some sensitivity analysis comparing the results for different group specifications and the models' out-of-sample forecasting performance confirms our model specification.

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Bibliographic Info

Paper provided by Oesterreichische Nationalbank (Austrian Central Bank) in its series Working Papers with number 85.

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Length: 43
Date of creation: 28 Jul 2003
Date of revision:
Handle: RePEc:onb:oenbwp:85

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Related research

Keywords: Bank lending; clustering; forecasting; Markov switching; Markov chain Monte Carlo; panel data.;

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References

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  1. Asea, Patrick K. & Blomberg, Brock, 1998. "Lending cycles," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 89-128.
  2. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August.
  3. G.J. De Bondt, 1999. "Banks and monetary transmission in Europe: empirical evidence," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 52(209), pages 149-168.
  4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  5. Francesco Giavazzi, 1999. "The Transmission Mechanism of Monetary Policy in Europe: Evidence from Banks’ Balance Sheets," Working papers 99-20, Massachusetts Institute of Technology (MIT), Department of Economics.
  6. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
  7. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  8. Anil K Kashyap & Jeremy C. Stein, 1994. "The Impact of Monetary Policy on Bank Balance Sheets," NBER Working Papers 4821, National Bureau of Economic Research, Inc.
  9. G.J. De Bondt, 1999. "Banks and monetary transmission in Europe: empirical evidence," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 52(209), pages 149-168.
  10. Jeremy C. Stein & Anil K. Kashyap, 2000. "What Do a Million Observations on Banks Say about the Transmission of Monetary Policy?," American Economic Review, American Economic Association, vol. 90(3), pages 407-428, June.
  11. Azariadis, Costas & Smith, Bruce, 1998. "Financial Intermediation and Regime Switching in Business Cycles," American Economic Review, American Economic Association, vol. 88(3), pages 516-36, June.
  12. Anil K Kashyap & Jeremy C. Stein, 1997. "What Do a Million Banks Have to Say About the Transmission of Monetary Policy?," NBER Working Papers 6056, National Bureau of Economic Research, Inc.
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Cited by:
  1. Werner Hölzl & Sögner Leopold, 2004. "Entry and Exit Dynamics in Austrian Manufacturing," Working Papers geewp36, Vienna University of Economics Research Group: Growth and Employment in Europe: Sustainability and Competitiveness.

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