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

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  • 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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Citations

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Cited by:
  1. 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.
  2. 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.
  3. Kai Carstensen & Klaus Wohlrabe & Christina Ziegler, 2011. "Predictive Ability of Business Cycle Indicators under Test - A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 82-106, February.
  4. Pär Österholm, 2010. "Improving Unemployment Rate Forecasts Using Survey Data," Finnish Economic Papers, Finnish Economic Association, vol. 23(1), pages 16-26, Spring.
  5. David Ubilava & Matt Holt, 2013. "El Niño southern oscillation and its effects on world vegetable oil prices: assessing asymmetries using smooth transition models," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(2), pages 273-297, 04.
  6. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Society for Computational Economics, vol. 43(2), pages 183-197, February.
  7. Ubilava, David & Helmers, Claes Gustav, 2011. "The ENSO Impact on Predicting World Cocoa Prices," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103528, Agricultural and Applied Economics Association.
  8. 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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