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Forecasting Regional Employment with the ENTROP Method


  • Uwe Blien
  • Alexandros Tassinopoulos


The paper provides an outline of a method useful for forecasting problems. The approach is based on a combination of top-down and bottom-up techniques. It is applied to project employment in all 327 (western) German districts for a time span of two years. The most important step in the preparation of the forecast uses the ENTROP method, which is an entropy optimizing procedure, a generalization of common RAS techniques, newly developed for the estimation of matrices from heterogeneous information. In a defined sense the estimated matrix is the most probable one. The method chosen is very flexible and uses any available information extensively. Therefore, the estimates are reliable as is shown in an ex-post forecast. There is a double purpose for the forecast of employment. First, it helps to gain insights in the causal processes generating regional developments and spatial disparities on labour markets. Second, it is useful for regional labour market policies, e.g. the budgetary planning of the Federal Employment Services. Cet article cherche a esquisser une methode qui repond aux problemes de la prevision. La methode est fondee sur une combinaison des techniques descendantes et ascendantes. On s'en sert afin de prevoir sur une periode de deux annees l'emploi dans chacun des 327 districts a l'ouest de l'Allemagne. Le stade preliminaire le plus important utilise la methode ENTROP, une methode qui optimise l'entropie, une generalisation des techniques RAS courantes, nouvellement developpees pour estimer les matrices de l'information heterogene. Du point de vue de sa definition, la matrice estimee s'averera la plus probable. La methode choisie est tres flexible et emploie beaucoup toute information disponible. Ainsi, les estimations sont sures, dont fait preuve une prevision ex post. La raison d'etre de la prevision de l'emploi est double. En premier, on arrive a mieux connaitre les causes du developpement regional et des ecarts regionaux du marche du travail. En deuxieme, elle guide les politiques regionales en faveur de l'emploi, a savoir la planification budgetaire des agences pour l'emploi federales. Dieser Aufsatz liefert den Umriss einer nutzlichen Methode zur Erstellung von Prognosen. Der Ansatz stutzt sich auf eine Kombination von topdown und bottom-up Techniken. Er wird dazu benutzt, die sozialversicherungspflichtige Beschaftigung in allen 327 (West) deutschen Landkreisen fur die Zeitspanne von 2 Jahren zu projizieren. Der wichtigste Schritt bei der Vorbereitung der Prognose stutzt sich auf die ENTROP Methode, ein Verfahren zur Optimierung der Entropie. Dabei handelt es sich um eine Verallgemeinerung gewohnlicher RAS Techniken, eine Neuentwicklung zur Schatzung von Matrizen aus heterogenen Daten. In gewisser Hinsicht ist die geschatzte Matrix die wahtscheinlichste von allen zulassigen. Die gewahlte Methode ist sehr flexibel und macht ausgiebig Gebrauch von allen verfugbaren Daten. Die Schatzungen sind darum zuverlassig, wie an einer ex-post Prognose gezeigt wird. Die Prognose der Beschaftigung verfolgt zwei Zwecke: erstens tragt sie dazu bei, Einsichten in die Kausalvorgange zu gewinnen, welche regionalen Entwicklungen und raumliche Disparitaten in Arbeitsmarkten zu Grunde liegen. Zweitens ist sie nutzlich fur die Planung der regionalen Arbeitsmarktpolitik, z. B. fur die Haushaltsplanung der Bundesanstalt fur Arbeit.

Suggested Citation

  • Uwe Blien & Alexandros Tassinopoulos, 2001. "Forecasting Regional Employment with the ENTROP Method," Regional Studies, Taylor & Francis Journals, vol. 35(2), pages 113-124.
  • Handle: RePEc:taf:regstd:v:35:y:2001:i:2:p:113-124
    DOI: 10.1080/00343400120033106

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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets," Experimental 0511001, EconWPA.
    3. 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.
    4. Raymond Struyk & Douglas Wissoker & Ioulia Zaitseva, 2004. "Economic Forecasting for Large Russian Cities," ERSA conference papers ersa04p318, European Regional Science Association.
    5. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    6. Simonetta Longhi & Peter Nijkamp, 2005. "Forecasting Regional Labour Market Developments Under Spatial Heterogeneity and Spatial Autocorrelation," Tinbergen Institute Discussion Papers 05-041/3, Tinbergen Institute.
    7. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
    8. Longhi, Simonetta & Nijkamp, Peter & Reggiani, Aura & Blien, Uwe, 2002. "Forecasting regional labour markets in Germany: an evaluation of the performance of neural network analysis," ERSA conference papers ersa02p117, European Regional Science Association.
    9. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.


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