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Was the Recent Downturn in US GDP Predictable?

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  • Mehmet Balcilar

    ()
    (Department of Economics, Eastern Mediterranean University)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Anandamayee Majumdar

    ()
    (Department of Biostatistics, University of North Texas Health Science Center)

  • Stephen M. Miller

    ()
    (Department of Economics, University of Nevada, Las Vegas)

Abstract

This paper uses small set of variables-- real GDP, the inflation rate, and the short-term interest rate -- and a rich set of models -- athoeretical and theoretical, linear and nonlinear, as well as classical and Bayesian models -- to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance by root mean squared errors of the models to the benchmark random-walk model, the two theoretical models, especially the nonlinear model, perform well on the average across all forecast horizons in out-of-sample forecasts, although at specific forecast horizons certain nonlinear athoeretical models perform the best. The nonlinear theoretical model also dominates in our ex ante forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic-stochastic-general-equilibrium models of the economy, may prove crucial in forecasting turning points.

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File URL: http://web.unlv.edu/projects/RePEc/pdf/1210.pdf
File Function: First version, 2012
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Bibliographic Info

Paper provided by University of Nevada, Las Vegas , Department of Economics in its series Working Papers with number 1210.

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Length: 47 pages
Date of creation: Dec 2012
Date of revision:
Handle: RePEc:nlv:wpaper:1210

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Keywords: Forecasting; Linear and non-linear models; Great Recession;

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