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Labour market forecasting : is disaggregation useful?

Author

Listed:
  • Weber, Enzo

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

  • Zika, Gerd

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

Abstract

"Using the example of short-term forecasts for German employment figures, the article at hand examines the question whether the use of disaggregated information increases the forecast accuracy of the aggregate. For this purpose, the out-of-sample forecasts for the aggregated employment forecast are compared to and contrasted with forecasts based on a vector-autoregressive model, which includes not only the aggregate but also the numbers of gainfully employed people at the industry level. The Clark/West test is used in the model comparison. It becomes evident that disaggregation significantly improves the employment forecast. Moreover, fluctuation- window tests help identify the phases during which disaggregation increases forecast accuracy to the strongest extent." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Weber, Enzo & Zika, Gerd, 2013. "Labour market forecasting : is disaggregation useful?," IAB Discussion Paper 201314, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  • Handle: RePEc:iab:iabdpa:201314
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    File URL: http://doku.iab.de/discussionpapers/2013/dp1413.pdf
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    References listed on IDEAS

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    1. Raffaella Giacomini & Barbara Rossi, 2016. "Model Comparisons In Unstable Environments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57, pages 369-392, May.
    2. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Sabine Klinger & Enzo Weber, 2016. "Decomposing Beveridge Curve Dynamics By Correlated Unobserved Components," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 877-894, December.
    5. Johann Fuchs & Enzo Weber, 2013. "A new look at the discouragement and the added worker hypotheses: applying a trend--cycle decomposition to unemployment," Applied Economics Letters, Taylor & Francis Journals, vol. 20(15), pages 1374-1378, October.
    6. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Pauser, Johannes, 2013. "Capital mobility, imperfect labour markets, and the provision of public goods," IAB Discussion Paper 201309, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    10. Michael Stops, 2014. "Job matching across occupational labour markets," Oxford Economic Papers, Oxford University Press, vol. 66(4), pages 940-958.
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    Citations

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    Cited by:

    1. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0002-5 is not listed on IDEAS
    2. Schwengler, Barbara, 2013. "Einfluss der europäischen Regionalpolitik auf die deutsche Regionalförderung," IAB Discussion Paper 201318, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    4. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    5. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ERSA conference papers ersa15p756, European Regional Science Association.
    6. repec:taf:apeclt:v:24:y:2017:i:4:p:279-283 is not listed on IDEAS
    7. R. Lehmann & K. Wohlrabe, 2017. "Experts, firms, consumers or even hard data? Forecasting employment in Germany," Applied Economics Letters, Taylor & Francis Journals, vol. 24(4), pages 279-283, February.
    8. Bauer, Angela & Kruppe, Thomas, 2013. "Policy Styles : zur Genese des Politikstilkonzepts und dessen Einbindung in Evaluationsstudien," IAB Discussion Paper 201322, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    More about this item

    Keywords

    Arbeitsmarktprognose - Methode; Prognostik; Beschäftigtenzahl;

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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