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

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  • Roberto Patuelli
  • Aura Reggiani
  • Peter Nijkamp
  • Uwe Blien

Abstract

Abstract In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks 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 specified explicitly. 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 (NUTS 3) 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. RÉSUMÉ Nouvelles Méthodes de Prévisions Fondées sur les Réseaux Neuronaux Appliquées l'Emploi Régional: Une Analyse des Marchés du travail dans l'Allemagne Réunifiée Dans cet article, les auteurs ont développé une série de modèles utilisant les réseaux neuronaux (RN) pour calculer des prévisions à court terme des paramètres de l'emploi, par région allemande. Les RN sont des outils statistiques modernes fondés sur des algorithmes d'apprentissage, capables de traiter de grandes quantités de données. On s'intéresse de plus en plus aux RN car ils permettent de gérer efficacement des séries de données complexes, bien que la relation fonctionnelle entre les variables dépendantes et indépendantes n'est pas définie explicitement. Cet article compare deux méthodologies fondées sur les RN. D'abord, il utilise les RN pour prévoir l'emploi régional dans les deux régions anciennement appelées Allemagne de l'Ouest et Allemagne de l'Est. Chaque modèle réalisé calcule de simples estimations des taux de croissance d'emploi pour chaque district allemand, sur une durée de 2 ans. Puis, il calcule des prévisions complémentaires, en combinant la méthodologie RN avec une analyse shift-share (ASS). Comme l'ASS a pour but d'identifier les variations relevées sur le marché local du travail, on emploie les résultats obtenus comme variables indépendantes complémentaires dans les modèles RN. Notre échantillon de données utilisé dans nos expériences se compose de 439 districts allemands. Comme les districts composant l’échantillon présentent de grandes différences en matière de taille et d'horizon temporel, les prévisions pour l'Allemagne de l'Ouest et l'Allemagne de l'Est sont calculées séparément. La capacité des modèles à établir des prévisions hors – échantillon est évaluée avec différents indicateurs statistiques appropriés. RESUMEN Nuevos métodos de redes neurales para la previsión de empleo regional: un análisis para los mercados laborales de Alemania En este documento desarrollamos una serie de modelos de redes neurales (RN) para calcular las previsiones a corto plazo de los modelos de empleo regional en Alemania. Las RN son modernas herramientas de estadísticas basadas en algoritmos de aprendizaje capaces de procesar un gran número de datos. Las RN se están popularizando cada vez más en diferentes campos porque son capaces de manejar grupos de datos complejos cuando la relación funcional entre las variables dependientes e independientes no está explícitamente especificada. En este artículo comparamos dos metodologías de RN. Primero, utilizamos las RN para pronosticar el empleo regional en Alemania del oeste y del este. Cada modelo aplicado computa por separado los cálculos de las tasas de crecimiento de empleo para cada distrito alemán, con un intervalo de previsión de 2 años. Luego se calculan las previsiones adicionales combinando la metodología de las RN con el análisis shift-share. Dado que los análisis shift-share identifican las variaciones observadas entre los distritos laborales, sus resultados se utilizan como otras variables explicatorios en los modelos de RN. El grupo de datos utilizado en nuestros experimentos abarca un panel de 439 distritos alemanes. Las previsiones para Alemania del oeste y este se computan por separado debido a las diferencias en los horizontes de tamaño y tiempo de los datos. La capacidad de previsión a partir de las muestras en los modelos es evaluada mediante varios indicadores adecuados de estadísticas.

Suggested Citation

  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
  • Handle: RePEc:taf:specan:v:1:y:2006:i:1:p:7-30
    DOI: 10.1080/17421770600661568
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    8. Esteban-Marquillas, J. M., 1972. "I. A reinterpretation of shift-share analysis," Regional and Urban Economics, Elsevier, vol. 2(3), pages 249-255, October.
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    10. John Cooper, 1999. "Artificial neural networks versus multivariate statistics: An application from economics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 909-921.
    11. 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.
    12. M M Fischer, 1998. "Computational Neural Networks: A New Paradigm for Spatial Analysis," Environment and Planning A, , vol. 30(10), pages 1873-1891, October.
    13. 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.
    14. Baker, Bruce D. & Richards, Craig E., 1999. "A comparison of conventional linear regression methods and neural networks for forecasting educational spending," Economics of Education Review, Elsevier, vol. 18(4), pages 405-415, October.
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    Cited by:

    1. 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 & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 329-356, August.
    2. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    3. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
    4. 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.
    5. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers 06-049/3, Tinbergen Institute.
    6. Jean‐François Ruault & Yves Schaeffer, 2020. "Scalable shift‐share analysis: Novel framework and application to France," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1667-1690, December.
    7. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    8. 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.
    9. Constantin Ilie & Margareta Ilie, 2022. "Brief Analysis of the Evolution of Female Employees in Recent Years. Research Using Mathematical Modelling," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 591-597, September.
    10. Matthias Firgo & Oliver Fritz, 2017. "Does having the right visitor mix do the job? Applying an econometric shift-share model to regional tourism developments," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 469-490, May.
    11. repec:dgr:vuarem:2009-14 is not listed on IDEAS
    12. 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.

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    More about this item

    Keywords

    Neural networks; forecasts; regional employment; shift-share analysis; shift-share regression; C23; E27; R12;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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