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Is economic recovery a myth? Robust estimation of impulse responses

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  • Coen Teulings

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  • Nick Zubanov

    ()

Abstract

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.

Suggested Citation

  • Coen Teulings & Nick Zubanov, 2011. "Is economic recovery a myth? Robust estimation of impulse responses," CPB Discussion Paper 131, CPB Netherlands Bureau for Economic Policy Analysis.
  • Handle: RePEc:cpb:discus:131
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    References listed on IDEAS

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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. Yanping Chong & Òscar Jordà & Alan M. Taylor, 2012. "The Harrod–Balassa–Samuelson Hypothesis: Real Exchange Rates And Their Long‐Run Equilibrium," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 609-634, May.
    3. John Y. Campbell & N. Gregory Mankiw, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, Oxford University Press, pages 857-880.
    4. Jon Faust & Jonathan H. Wright, 2008. "Efficient Prediction of Excess Returns," NBER Working Papers 14169, National Bureau of Economic Research, Inc.
    5. Valerie Cerra & Sweta Chaman Saxena, 2008. "Growth Dynamics: The Myth of Economic Recovery," American Economic Review, American Economic Association, vol. 98(1), pages 439-457, March.
    6. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    7. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    8. 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.
    9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    10. 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.
    11. 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.
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    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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