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Unemployment Forecasts: Room for Improvement?

Author

Listed:
  • Yvonne Adema

    (CPB Netherlands Bureau of Economic Policy Analysis)

  • Kees Folmer

    (CPB Netherlands Bureau of Economic Policy Analysis)

  • Gerrit Hugo Heuvelen

    (CPB Netherlands Bureau of Economic Policy Analysis)

  • Sonny Kuijpers

    (CPB Netherlands Bureau of Economic Policy Analysis)

  • Rob Luginbuhl

    (CPB Netherlands Bureau of Economic Policy Analysis)

  • Bas Scheer

    (CPB Netherlands Bureau of Economic Policy Analysis)

Abstract

During the crisis of 2009–2013 many institutes made large errors in their unemployment forecasts. This paper develops alternative short-run models to improve these forecasts. The models are applied to the Netherlands and compared to the unemployment forecasts of the CPB. A BVAR model performs significantly better than the CPB forecasts. The BVAR can also be used to predict the labor market transition probabilities used in the forecast functions of two-state and three-state stock-flow models of unemployment. This also results in significantly better forecasts. However the combination of these three best-performing models produces the largest gain in forecast accuracy.

Suggested Citation

  • Yvonne Adema & Kees Folmer & Gerrit Hugo Heuvelen & Sonny Kuijpers & Rob Luginbuhl & Bas Scheer, 2020. "Unemployment Forecasts: Room for Improvement?," De Economist, Springer, vol. 168(3), pages 403-417, September.
  • Handle: RePEc:kap:decono:v:168:y:2020:i:3:d:10.1007_s10645-020-09363-0
    DOI: 10.1007/s10645-020-09363-0
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    References listed on IDEAS

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    1. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    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. 43(2 (Fall)), pages 83-131.
    3. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    4. Michael W. L. Elsby & Bart Hobijn & Ayşegül Şahin, 2013. "Unemployment Dynamics in the OECD," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 530-548, May.
    5. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    6. Adam Elbourne & Henk Kranendonk & Rob Luginbuhl & Bert Smid & Martin Vromans, 2008. "Evaluating CPB's published GDP growth forecasts; a comparison with individual and pooled VAR based forecasts," CPB Document 172.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    7. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    8. Silva, José I. & Vázquez-Grenno, Javier, 2013. "The ins and outs of unemployment in a two-tier labor market," Labour Economics, Elsevier, vol. 24(C), pages 161-169.
    9. Adam Elbourne & Henk Kranendonk & Rob Luginbuhl & Bert Smid & Martin Vromans, 2008. "Evaluating CPB's published GDP growth forecasts; a comparison with individual and pooled VAR based forecasts," CPB Document 172, CPB Netherlands Bureau for Economic Policy Analysis.
    10. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    11. Barnichon, Regis & Garda, Paula, 2016. "Forecasting unemployment across countries: The ins and outs," European Economic Review, Elsevier, vol. 84(C), pages 165-183.
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    More about this item

    Keywords

    BVAR; Forecasting; Okun’s law; Stock-flow models; Unemployment;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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