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Is Economic Recovery a Myth? Robust Estimation of Impulse Responses

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  • Teulings, Coen N
  • Zubanov, Nick

Abstract

There is a lively debate on the persistence of the current banking crisis' impact on GDP. Impulse Response Functions (IRF) estimated by Cerra and Saxena (2008) suggest that the effects of earlier crises were long-lasting. We show that standard estimates of IRFs are highly sensitive to misspecification of the underlying data generation process. Direct estimation of IRFs by a methodology similar to Jorda's (2005) local projection method is robust to misspecifications of the data generation process but yields biased estimates when country fixed effects are added. We propose a simple method to deal with this bias, which we apply to panel data from 99 countries for the period 1974-2001. Our estimates suggest that an average banking crisis leads to an output loss of around 10 percent with little sign of recovery. GDP losses from banking crises are more severe for African countries and economies in transition.

Suggested Citation

  • Teulings, Coen N & Zubanov, Nick, 2010. "Is Economic Recovery a Myth? Robust Estimation of Impulse Responses," CEPR Discussion Papers 7800, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:7800
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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, vol. 102(4), 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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    More about this item

    Keywords

    banking crisis; impulse response; panel data;

    JEL classification:

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

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