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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Tinbergen Institute Discussion Paper ; No. 06-020/3

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models ; Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications ; Size and Spatial Distributions of Regional Economic Activity
• Thema: networks ; forecasts ; regional employment ; shift-share analysis ; shift-share regression

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Patuelli, Roberto ; Reggiani, Aura ; Nijkamp, Peter ; Blien, Uwe
• Ereignis: Veröffentlichung
• (wer): Tinbergen Institute
• (wo): Amsterdam and Rotterdam
• (wann): 2006

Weitere Informationen

• Handle: http://hdl.handle.net/10419/86307
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ