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Was the Recent Downturn in US GDP Predictable?

Mehmet Balcilar, Rangan Gupta, Anandamayee Majumdar and Stephen Miller

No 2012-38, Working papers from University of Connecticut, Department of Economics

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

Pages: 48 pages
Date: 2012-11, Revised 2013-12
New Economics Papers: this item is included in nep-for and nep-mac
Note: Stephen Miller is corresponding author
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