[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_3027.html
   My bibliography  Save this paper

Is Economic Recovery a Myth? Robust Estimation of Impulse Responses

Author

Listed:
  • Coenraad N. Teulings
  • Nick Zubanov
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

  • Coenraad N. Teulings & Nick Zubanov, 2010. "Is Economic Recovery a Myth? Robust Estimation of Impulse Responses," CESifo Working Paper Series 3027, CESifo.
  • Handle: RePEc:ces:ceswps:_3027
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp3027.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. John Y. Campbell & N. Gregory Mankiw, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(4), pages 857-880.
    5. 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.
    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. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    9. 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.
    10. Den Haan, Wouter & Cai, Xiaoming, 2009. "Predicting recoveries and the importance of using enough information," CEPR Discussion Papers 7508, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Afonso, António & Jalles, João Tovar, 2019. "The Fiscal consequences of deflation: Evidence from the Golden Age of Globalization," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 129-147.
    2. Òscar Jordà & Moritz Schularick & Alan M. Taylor, 2011. "When credit bites back: leverage, business cycles, and crises," Working Paper Series 2011-27, Federal Reserve Bank of San Francisco.
    3. MAÏ ASSAN CHEDI, Maman, 2022. "Does Defence Expenditure Affect Education and Health expenditures in Saharan Africa?," African Journal of Economic Review, African Journal of Economic Review, vol. 10(4), September.
    4. Daron Acemoglu & Suresh Naidu & Pascual Restrepo & James A. Robinson, 2019. "Democracy Does Cause Growth," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 47-100.
    5. Riccardo Colacito & Bridget Hoffmann & Toan Phan, 2019. "Temperature and Growth: A Panel Analysis of the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(2-3), pages 313-368, March.
    6. Furceri, Davide & Zdzienicka, Aleksandra, 2012. "How costly are debt crises?," Journal of International Money and Finance, Elsevier, vol. 31(4), pages 726-742.
    7. Alloza, Mario & Sanz, Carlos, 2019. "Dynamic Effects of Persistent Shocks," UC3M Working papers. Economics 29187, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    9. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.
    10. Sheida Teimouri, 2015. "Currency crises and dynamics of real wages," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 151(2), pages 377-403, May.
    11. Mohcine Bakhat & José M. Labeaga & Xavier Labandeira & Xiral Lñpez, 2013. "Economic Crisis and Elasticities of Car Fuels: Evidence for Spain," Working Papers fa15-2013, Economics for Energy.
    12. Breitung, Jörg & Kripfganz, Sebastian & Hayakawa, Kazuhiko, 2022. "Bias-corrected method of moments estimators for dynamic panel data models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 116-132.
    13. Beutler, Toni & Bichsel, Robert & Bruhin, Adrian & Danton, Jayson, 2020. "The impact of interest rate risk on bank lending," Journal of Banking & Finance, Elsevier, vol. 115(C).
    14. Davide Furceri & Aleksandra Zdzienicka, 2012. "The Consequences of Banking Crises for Public Debt," International Finance, Wiley Blackwell, vol. 15(3), pages 289-307, December.
    15. Cave, Joshua & Chaudhuri, Kausik & Kumbhakar, Subal C., 2023. "Dynamic firm performance and estimator choice: A comparison of dynamic panel data estimators," European Journal of Operational Research, Elsevier, vol. 307(1), pages 447-467.
    16. António Afonso & João Tovar Jalles, 2017. "The Price Relevance of Fiscal Developments," International Economic Journal, Taylor & Francis Journals, vol. 31(1), pages 36-50, January.
    17. Olaf J de Groot & Carlos Bozzoli & Anousheh Alamir & Tilman Brück, 2022. "The global economic burden of violent conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 259-276, March.
    18. Ant�nio Afonso & Jalles, 2016. "Markups' cyclical behaviour: the role of demand and supply shocks," Applied Economics Letters, Taylor & Francis Journals, vol. 23(1), pages 1-5, January.
    19. Norkutė, Milda & Westerlund, Joakim, 2019. "The factor analytical method for interactive effects dynamic panel models with moving average errors," Econometrics and Statistics, Elsevier, vol. 11(C), pages 83-104.
    20. Erick Rangel Gonzalez & Irving Llamosas-Rosas & Felipe J. Fonseca, 2021. "Aislamiento social y el COVID-19 en las regiones de Mexico," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 18(2), pages 1-22, Julio-Dic.

    More about this item

    Keywords

    banking crisis; impulse response; panel data;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_3027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.