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Is economic recovery a myth? Robust estimation of impulse responses

  • Coen Teulings
  • Nick Zubanov

We apply a robust method to the estimation of Impulse Response Functions (IRFs) to paneldata for 99 countries for the period 1974-2001. There is a lively debate on the persistence of the current banking crisis’ impact on output. IRFs estimated by Cerra and Saxena (2008) suggest that these effects will be long lasting. However, standard estimates of IRFs are highly sensitive to slight degrees of misspecification. Moreover, adding fixed effects complicates inference on persistence. Direct estimation of IRFs by a method similar to the local projection method of Jorda (2005) is robust to these specification errors. Our estimates suggest that an average banking crisis leads to an output loss of up to up to 9 percent, without any recovery within seven years. There are some indications for recovery in later years, but these are insignificant. We find some evidence for heterogeneity in the effects of a banking crisis.

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Paper provided by CPB Netherlands Bureau for Economic Policy Analysis in its series CPB Discussion Paper with number 131.

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Date of creation: May 2011
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Handle: RePEc:cpb:discus:131
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  1. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
  2. Chong, Yanping & Jordà, Òscar & Taylor, Alan M., 2010. "The Harrod-Balassa-Samuelson Hypothesis: Real Exchange Rates and their Long-Run Equilibrium," CEPR Discussion Papers 7902, C.E.P.R. Discussion Papers.
  3. Valerie Cerra & Sweta Chaman Saxena, 2008. "Growth Dynamics: The Myth of Economic Recovery," American Economic Review, American Economic Association, vol. 98(1), pages 439-57, March.
  4. Jon Faust & Jonathan H. Wright, 2008. "Efficient Prediction of Excess Returns," NBER Working Papers 14169, National Bureau of Economic Research, Inc.
  5. Campbell, John & Mankiw, Gregory, 1987. "Are Output Fluctuations Transitory?," Scholarly Articles 3122545, Harvard University Department of Economics.
  6. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
  7. Jon Faust & Jonathan H. Wright, 2011. "Efficient Prediction of Excess Returns," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 647-659, May.
  8. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
  9. Cai, Xiaoming & Den Haan, Wouter, 2009. "Predicting recoveries and the importance of using enough information," CEPR Discussion Papers 7508, C.E.P.R. Discussion Papers.
  10. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
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