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New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets

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Author Info

  • Roberto Patuelli

    ()
    (Department of Spatial Economics, Vrije Universiteit Amsterdam)

  • Aura Reggiani

    ()
    (Department of Economics, University of Bologna, Italy)

  • Peter Nijkamp

    ()
    (Department of Spatial Economics, Vrije Universiteit Amsterdam)

  • Uwe Blien

    ()
    (Institut f�r Arbeitsmarkt und Berufsforschung (IAB), Nuremberg)

Abstract

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. NNs are modern statistical tools based on learning algorithms that are able to process large amounts of data. NNs are enjoying increasing interest in several fields, because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not explicitly specified. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with Shift-Share Analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 06-020/3.

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Date of creation: 17 Feb 2006
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Handle: RePEc:dgr:uvatin:20060020

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Web page: http://www.tinbergen.nl

Related research

Keywords: networks; forecasts; regional employment; shift-share analysis; shift-share regression;

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References

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  1. Nijkamp, Peter & Reggiani, Aura & Tsang, Wai Fai, 1999. "Comparative modelling of interregional transport flows : applications to multimodal European freight transport," Serie Research Memoranda 0002, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  2. Uwe Blien & Katja Wolf, 2002. "Regional development of employment in eastern Germany: an analysis with an econometric analogue to shift-share techniques," Papers in Regional Science, Springer, vol. 81(3), pages 391-414.
  3. Swanson, N.R. & White, H., 1995. "A Models Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Papers 04-95-12, Pennsylvania State - Department of Economics.
  4. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
  5. Suahasil Nazara & Geoffrey J.D. Hewings, 2004. "Spatial Structure and Taxonomy of Decomposition in Shift-Share Analysis," Growth and Change, Gatton College of Business and Economics, University of Kentucky, vol. 35(4), pages 476-490.
  6. Lutz Bellmann & Uwe Blien, 2001. "Wage curve analyses of establishment data from western Germany," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 54(4), pages 851-863, July.
  7. 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.
  8. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  9. Matías Mayor Fernández & Ana Jesús López Menéndez, 2005. "The spatial shift-share analysis - new developments and some findings for the Spanish case," ERSA conference papers ersa05p659, European Regional Science Association.
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Citations

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Cited by:
  1. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Working Paper Series 02_09, The Rimini Centre for Economic Analysis, revised May 2010.
  2. Patuelli, R. & Reggiani, A. & Nijkamp, P., 2009. "Neural networks for cross-sectional employment forecasts: a comparison of model specifications for germany," Serie Research Memoranda 0014, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  3. Buda, Rodolphe, 2008. "Estimation de l'emploi régional et sectoriel salarié français : application à l'année 2006
    [Estimation of the french salaried regional and sectoral employment: application to the year 2006]
    ," MPRA Paper 34881, University Library of Munich, Germany.
  4. 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.
  5. Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2008. "Regional unemployment forecasts with spatial interdependencies," IAB Discussion Paper 200828, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  6. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper Series 07_09, The Rimini Centre for Economic Analysis, revised Feb 2010.
  7. Gian Zaccomer & Pamela Mason, 2011. "A new spatial shift-share decomposition for the regional growth analysis: a local study of the employment based on Italian Business Statistical Register," Statistical Methods and Applications, Springer, vol. 20(3), pages 329-356, August.

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