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Forecasting US GNP Growth: The Role of Uncertainty

Author

Listed:
  • Mawuli Segnon

    (Department of Economics, University of Münster, Germany)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, South Africa)

  • Stelios Bekiros

    (Department of Economics, European University Institute, Florence, Italy)

  • Mark E. Wohar

    (Department of Economics, University of Nebraska, Omaha, USA and School of Business and Economics, Loughborough University, UK)

Abstract
There are a large number of models developed in the literature to analyse and forecast the changes in output dynamics. The objective of this paper is to compare the forecasting ability of uni- and bivariate models in terms of forecasting U.S. GNP growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as the superior predictive ability (SPA) and equal accuracy tests, we evaluate the forecasting performance of our models over the quarterly period of 1919:2-2014:4, given an in-sample of 1900:1 1919:1. We find that the economic policy uncertainty index should be improving the accuracy of U.S. GNP growth forecasts in the bivariate models. While we find that the Markov switching time varying parameter VAR (MS-TVP-VAR) models in most cases cannot be outperformed its competitive models according to the root mean squared error (RMSE), the density forecast measure reveals that the Bayesian VAR (BVAR) model with stochastic volatility in most cases is the model that produces more accurate forecasts. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rate.

Suggested Citation

  • Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2016. "Forecasting US GNP Growth: The Role of Uncertainty," Working Papers 201667, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201667
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    More about this item

    Keywords

    Forecast comparison; vector autoregressive models; US GNP; Economic Policy Uncertainty;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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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