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Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts

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Author Info

  • Costas Milas

    ()
    (Keele University, UK and The Rimini Centre for Economics Analysis, Italy.)

  • Philip Rothman

    ()
    (East Carolina University, USA)

Abstract

In this paper we use smooth transition vector error-correction models (STVECMs) in a simulated out-of-sample forecasting experiment for the unemployment rates of the four non-Euro G-7 countries, the U.S., U.K., Canada, and Japan. For the U.S., pooled forecasts constructed by taking the median value across the point forecasts generated by the linear and STVECM forecasts appear to perform better than the linear AR(p) benchmark more so during business cycle expansions. Such pooling also tends to lead to statistically significant forecast improvement for the U.K. ÒReality checksÓ of these results suggest that they do not stem from data snooping.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 49-07.

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Date of creation: Jul 2007
Date of revision: Jul 2007
Handle: RePEc:rim:rimwps:49-07

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Keywords: nonlinear; asymmetric; STVECM; pooled forecasts; Diebold-Mariano;

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References

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Citations

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Cited by:
  1. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2010. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Discussion Papers in Economics 11442, University of Munich, Department of Economics.
  2. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, Research Program on Forecasting.
  3. Regis Barnichon & Christopher J. Nekarda, 2012. "The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 83-131.
  4. Österholm, Pär, 2009. "Improving Unemployment Rate Forecasts Using Survey Data," Working Paper 112, National Institute of Economic Research.
  5. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.

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