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Was the Recent Downturn in US GDP Predictable?

  • Mehmet Balcilar

    (Eastern Mediterranean University)

  • Rangan Gupta

    (University of Pretoria)

  • Anandamayee Majumdar

    (University of North Texas Health Science Center)

  • Stephen M. Miller

    (University of Nevada, Las Vegas and University of Connecticut)

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. JEL Classification: C32, E37 Key words: Forecasting, Linear and non-linear models, Great Recession

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2012-38.

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Length: 48 pages
Date of creation: Nov 2012
Date of revision: Dec 2013
Handle: RePEc:uct:uconnp:2012-38
Note: Stephen Miller is corresponding author
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