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Regional employment forecasts with spatial interdependencies

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  • Hampel, Katharina

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

  • Kunz, Marcus
  • Schanne, Norbert

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

  • Wapler, Rüdiger

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

  • Weyh, Antje

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

Abstract

"The labour-market policy-mix in Germany is increasingly being decided on a regional level. This requires additional knowledge about the regional development which (disaggregated) national forecasts cannot provide. Therefore, we separately forecast employment for the 176 German labour- market districts on a monthly basis. We first compare the prediction accuracy of standard time-series methods: autoregressive integrated moving averages (ARIMA), exponentially weighted moving averages (EWMA) and the structural-components approach (SC) in these small spatial units. Second, we augment the SC model by including autoregressive elements (SCAR) in order to incorporate the influence of former periods of the dependent variable on its current value. Due to the importance of spatial interdependencies in small labour-market units, we further augment the basic SC model by lagged values of neighbouring districts in a spatial dynamic panel (SCSAR). The prediction accuracies of the models are compared using the mean absolute percentage forecast error (MAPFE) for the simulated out-of-sample forecast for 2005. Our results show that the SCSAR is superior to the SCAR and basic SC model. ARIMA and EWMA models perform slightly better than SCSAR in many of the German labour-market districts. This reflects that these two moving-average models can better capture the trend reversal beginning in some regions at the end of 2004. All our models have a high forecast quality with an average MAPFE lower than 2.2 percent." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  • Handle: RePEc:iab:iabdpa:200702
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    Cited by:

    1. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
    2. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2012. "Persistence Of Regional Unemployment: Application Of A Spatial Filtering Approach To Local Labor Markets In Germany," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 300-323, May.
    3. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    4. Alfred Garloff & Carsten Pohl & Norbert Schanne, 2013. "Do small labor market entry cohorts reduce unemployment?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(15), pages 379-406, September.
    5. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    6. Norbert Schanne, 2011. "Forecasting Regional Labour Markets with GVAR Models and Indicators (refereed paper)," ERSA conference papers ersa10p1044, European Regional Science Association.
    7. repec:ura:ecregj:v:1:y:2017:i:2:p:410-421 is not listed on IDEAS
    8. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper series 15_12, Rimini Centre for Economic Analysis, revised Oct 2012.
    9. repec:eee:ecmode:v:67:y:2017:i:c:p:248-260 is not listed on IDEAS
    10. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    11. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 43, pages 29-51.
    12. Schanne, Norbert, 2012. "The formation of experts' expectations on labour markets : do they run with the pack?," IAB Discussion Paper 201225, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    13. Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
    14. Bourmpoula, Evangelia. & Wieser, Christina., 2014. "An accuracy assessment of the global employment trends' unemployment rate forecasts," ILO Working Papers 994888903402676, International Labour Organization.
    15. Rolf Schenker & Martin Straub, 2011. "Analyse der kantonalen Arbeitslosenquoten mittels räumlichen Zeitreihenmodellen," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 5(1), pages 75-87, March.
    16. Bourmpoula, Evangelia. & Wieser, Christina., 2014. "An accuracy assessment of the global employment trends' unemployment rate forecasts," Global Employment Trends Reports 994888903402676, International Labour Office, Economic and Labour Market Analysis Department.
    17. Hohendanner, Christian, 2007. "Verdrängen Ein-Euro-Jobs sozialversicherungspflichtige Beschäftigung in den Betrieben?," IAB Discussion Paper 200708, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    18. repec:ilo:ilowps:488890 is not listed on IDEAS
    19. Eckey, Hans-Friedrich & Schwengler, Barbara & Türck, Matthias, 2007. "Vergleich von deutschen Arbeitsmarktregionen," IAB Discussion Paper 200703, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    20. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    21. Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.
    22. Amend, Elke & Herbst, Patrick, 2008. "Labor market pooling and human capital investment decisions," IAB Discussion Paper 200804, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    23. Bruckmeier, Kerstin & Graf, Tobias & Rudolph, Helmut, 2008. "Working poor: Arm oder bedürftig? : eine Analyse zur Erwerbstätigkeit in der SGB-II-Grundsicherung mit Verwaltungsdaten," IAB Discussion Paper 200834, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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    Keywords

    regionaler Arbeitsmarkt; Beschäftigungsentwicklung; Prognoseverfahren; Arbeitsmarktprognose - Methode;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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