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Predicting a recovery date from the economic crisis of 2008


  • Azis, Iwan J.


Predicting a recovery from a crisis is always difficult, but it is particularly so with the 2008 crisis in the United States. How could a small segment of the financial markets known as subprime credit bring down the world's largest economy into the worst recession since WWII? The resulting conflicts in policy responses are so severe that the short-term objective (recovery) clashes with the longer-term and more structural goals (governance, regulations, technology). This and the enormous uncertainties caused by it add to the difficulties to predict the pace of recovery. While the economic turnaround depends on consumers' decision to spend and business' decision to invest and hire, in an uncertain situation such decisions can only be taken as a result of market players' perceptions of opportunity that depend on their emotional state and confidence. When the latter produces spontaneous urge to action ('animal spirits'), the recovery process accelerates. Thus, the appropriate model to predict recovery should be able to incorporate such perceptions factors. By identifying and prioritizing economic and policy factors, it is shown how such a model, the Analytic Network Process (ANP), can be used to make the prediction of the recovery time of the US economy. The forecast was made during Spring 2009 by the author working with participants in a seminar of "Economics of Financial Crisis" at Cornell University. We used an expert judgment approach within the framework of a decision theory model, based on the ANP structure that captures the interplay between financial market, housing sector, and market confidence, all of which are influenced by a range of policies. It is estimated that a real sustainable recovery will begin around late July or early August 2010. While a quicker recovery is possible given the enormous size of fiscal stimulus, monetary injection and unprecedented measures of qualitative easing, it is our conjecture that the temporary nature of all these measures will make such a quick turn-around unsustainable (a double-dip recession). When sensitivity analysis was performed, it was found that altering the priorities of the policies, and their interactions with the aggregate demand components, would not significantly change the estimated time to recovery. This stability of the prediction is due to the overriding importance of restoring confidence, making the other factors less important.

Suggested Citation

  • Azis, Iwan J., 2010. "Predicting a recovery date from the economic crisis of 2008," Socio-Economic Planning Sciences, Elsevier, vol. 44(3), pages 122-129, September.
  • Handle: RePEc:eee:soceps:v:44:y:2010:i:3:p:122-129

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    References listed on IDEAS

    1. Stephen G. Cecchetti, 2009. "Crisis and Responses: The Federal Reserve in the Early Stages of the Financial Crisis," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 51-75, Winter.
    2. George A. Akerlof, 2009. "How Human Psychology Drives the Economy and Why It Matters," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(5), pages 1175-1175.
    3. Daniel M. Covitz & J. Nellie Liang & Gustavo A. Suarez, 2009. "The evolution of a financial crisis: panic in the asset-backed commercial paper market," Finance and Economics Discussion Series 2009-36, Board of Governors of the Federal Reserve System (U.S.).
    4. Douglas W. Diamond & Raghuram G. Rajan, 2009. "The Credit Crisis: Conjectures about Causes and Remedies," American Economic Review, American Economic Association, vol. 99(2), pages 606-610, May.
    5. Blair, Andrew R. & Mandelker, Gershon N. & Saaty, Thomas L. & Whitaker, Rozann, 2010. "Forecasting the resurgence of the U.S. economy in 2010: An expert judgment approach," Socio-Economic Planning Sciences, Elsevier, vol. 44(3), pages 114-121, September.
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    Cited by:

    1. Li, Fangyi & Song, Zhouying & Liu, Weidong, 2014. "China's energy consumption under the global economic crisis: Decomposition and sectoral analysis," Energy Policy, Elsevier, vol. 64(C), pages 193-202.
    2. Banai, Reza & Wakolbinger, Tina, 2011. "A measure of regional influence with the analytic network process," Socio-Economic Planning Sciences, Elsevier, vol. 45(4), pages 165-173, December.


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