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Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts

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  • Milas, Costas
  • Rothman, Philip

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., the 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 perform better than the linear AR(p) benchmark, and more so during business cycle expansions. Such pooling leads to statistically significant forecast improvement for the U.K. across the business cycle. "Reality checks" of these results suggest that they do not stem from data snooping.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 24 (2008)
Issue (Month): 1 ()
Pages: 101-121

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Handle: RePEc:eee:intfor:v:24:y:2008:i:1:p:101-121

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Web page: http://www.elsevier.com/locate/ijforecast

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Citations

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Cited by:
  1. 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.
  2. Wohlrabe, Klaus & Carstensen, Kai & Ziegler, Christina, 2010. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Munich Reprints in Economics 19719, University of Munich, Department of Economics.
  3. 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.
  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. 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.
  6. Regis Barnichon & Christopher J. Nekarda, 2013. "The ins and outs of forecasting unemployment: Using labor force flows to forecast the labor market," Finance and Economics Discussion Series 2013-19, Board of Governors of the Federal Reserve System (U.S.).
  7. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
  8. 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.

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