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The external finance premium in the euro area A useful indicator for monetary policy?

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  • Gelain, Paolo

Abstract

In this paper I estimate a New Keynesian Dynamic Stochastic General Equilibrium model for the Euro Area, which closely follows the structure of the model developed by Smets and Wouters (2003, 2005, 2007), with the addition of the so-called financial accelerator mechanism developed in Bernanke, Gertler and Gilchrist (1999). The main aim is to obtain a time series for the unobserved external finance premium that entrepreneurs pay on their loans, with the further aim of providing a dynamic analysis of it. Results confirm in general what was recently found for the US by De Graeve (2008), namely that (1) the model incorporating financial frictions can generate a series for the premium, without using any financial macroeconomic aggregates, that is highly correlated with available proxies for it, (2) the estimated premium is not necessarily counter-cyclical (this depends on the shock considered). Nevertheless, although in addition the model with financial frictions better describes Euro Area data than the model without them, the former is not satisfactory in many other respects. For instance, the accelerator effect turns out to be statistically not significant. However, this does not impede financial frictions from remaining a key ingredient to model. In fact, I found that the estimated premium is a very powerful predictor of inflation. It overcomes, in terms of the Mean Squared Forecast Error, the traditional output gap measure in a Phillips curve specification. JEL Classification: E4, E5, E37

Suggested Citation

  • Gelain, Paolo, 2010. "The external finance premium in the euro area A useful indicator for monetary policy?," Working Paper Series 1171, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20101171
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    1. Frank Smets & Raf Wouters, 2004. "Forecasting with a Bayesian DSGE Model: An Application to the Euro Area," Journal of Common Market Studies, Wiley Blackwell, vol. 42(4), pages 841-867, November.
    2. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393 Elsevier.
    3. Selim Elekdag & Alejandro Justiniano & Ivan Tchakarov, 2006. "An Estimated Small Open Economy Model of the Financial Accelerator," IMF Staff Papers, Palgrave Macmillan, vol. 53(2), pages 1-2.
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    Cited by:

    1. Gross, Marco, 2011. "Corporate bond spreads and real activity in the euro area - Least Angle Regression forecasting and the probability of the recession," Working Paper Series 1286, European Central Bank.
    2. Merola, Rossana, 2015. "The role of financial frictions during the crisis: An estimated DSGE model," Economic Modelling, Elsevier, vol. 48(C), pages 70-82.
    3. Fabio Fornari & Livio Stracca, 2012. "What does a financial shock do? First international evidence," Economic Policy, CEPR;CES;MSH, pages 407-445.
    4. Smets, Frank & Villa, Stefania, 2016. "Slow recoveries: Any role for corporate leverage?," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 54-85.
    5. Villa, Stefania, 2016. "Financial Frictions In The Euro Area And The United States: A Bayesian Assessment," Macroeconomic Dynamics, Cambridge University Press, vol. 20(05), pages 1313-1340, July.
    6. Wieland, V. & Afanasyeva, E. & Kuete, M. & Yoo, J., 2016. "New Methods for Macro-Financial Model Comparison and Policy Analysis," Handbook of Macroeconomics, Elsevier.

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    Keywords

    Bayesian estimation; Euro Area External Finance Premium; financial accelerator; inflation; NK DSGE;

    JEL classification:

    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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