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Oil-Price Density Forecasts of U.S. GDP

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  • Francesco Ravazzolo
  • Philip Rothman

Abstract

We carry out a pseudo out-of-sample density forecasting study for U.S. GDP with an autoregressive benchmark and alternatives to the benchmark than include both oil prices and stochastic volatility. The alternatives to the benchmark produce superior density forecasts. This comparative density performance appears to be driven more by stochastic volatility than by oil prices. We use our density forecasts to compute a recession risk indicator around the Great Recession. The alternative model that includes the real price of oil generates the earliest strong signal of a recession; but it also shows increased recession risk after the Great Recession.

Suggested Citation

  • Francesco Ravazzolo & Philip Rothman, 2015. "Oil-Price Density Forecasts of U.S. GDP," Working Papers No 10/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    • Handle: RePEc:bny:wpaper:0038
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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