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Prognose der Anzahl der Erwerbspersonen: Eine Vorausberechnung auf Basis der Funktionalen Datenanalyse am Beispiel der Metropolregion Rhein-Neckar

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  • Deschermeier Philipp

    (Köln)

Abstract

Labour force projection for the Rhine-Neckar Metropolitan Area. Demographic change and the expected higher skills shortages both induce a growing need for regional labour market forecasts as quantitative decision-making bases for regional planning. This paper uses a two-stage model for projecting the labour force by single years of age and sex that is applied to the case of the Rhine-Neckar Metropolitan Area until 2030.

Suggested Citation

  • Deschermeier Philipp, 2014. "Prognose der Anzahl der Erwerbspersonen: Eine Vorausberechnung auf Basis der Funktionalen Datenanalyse am Beispiel der Metropolregion Rhein-Neckar," ZFW – Advances in Economic Geography, De Gruyter, vol. 58(1), pages 50-65, October.
  • Handle: RePEc:bpj:zfwige:v:58:y:2014:i:1:p:50-65:n:4
    DOI: 10.1515/zfw.2014.0004
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    References listed on IDEAS

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    1. Gary S. Becker, 1981. "A Treatise on the Family," NBER Books, National Bureau of Economic Research, Inc, number beck81-1.
    2. Ludwig, Volker & Pfeiffer, Friedhelm, 2006. "Abschreibungsraten allgemeiner und beruflicher Ausbildungsinhalte : empirische Evidenz auf Basis subjektiver Einschätzungen," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 27556, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    4. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    5. Deschermeier, Philipp & Müller, Eva M., 2012. "Analyse der Wohn‐ und Arbeitsortverteilung von Hochqualifizierten in der Metropolregion Rhein‐Neckar," Working Papers 12-09, University of Mannheim, Department of Economics.
    6. Oliver Lipps & Frank Betz, 2004. "Stochastic Population Projection for Germany," MEA discussion paper series 04059, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    7. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    8. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    9. Nico Keilman & Dinh Quang Pham & Arve Hetland, 2002. "Why population forecasts should be probabilistic - illustrated by the case of Norway," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 6(15), pages 409-454.
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