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

Author

Listed:
  • 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.

Suggested Citation

  • Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:49_07
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    5. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
    6. Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 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), De Gruyter, vol. 231(1), pages 82-106, February.
    7. Pär Österholm, 2010. "Improving Unemployment Rate Forecasts Using Survey Data," Finnish Economic Papers, Finnish Economic Association, vol. 23(1), pages 16-26, Spring.
    8. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    9. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    10. M. de Carvalho & K. F. Turkman & A. Rua, 2013. "Dynamic threshold modelling and the US business cycle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 535-550, August.
    11. Simionescu Mihaela, 2015. "Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?," Naše gospodarstvo/Our economy, Sciendo, vol. 61(3), pages 3-21, June.
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    14. Donya Rahmani & Damien Fay, 2022. "A state‐dependent linear recurrent formula with application to time series with structural breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 43-63, January.
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    16. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    17. 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.
    18. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 183-197, February.
    19. 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. 43(2 (Fall)), pages 83-131.
    20. Muneeb Ahmad & Yousaf Ali Khan & Chonghui Jiang & Syed Jawad Haider Kazmi & Syed Zaheer Abbas, 2023. "The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 528-543, January.
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