The aim of the present paper is to forecast regional employment developments in the 327 West-German districts. Using a Neural Networks (NNs) methodology we try to identify the existence of underlying structural relationships between the input variables - data on regional and sectoral employment and wages - and the future development of employment at a district level. In order to offer reliable forecasts for the years 2000 and 2001, a variety of NN models has been developed and compared. The emerging results confirm the ability of NNs in capturing the complex data structures - in the training and test phases - and hence in 'extrapolating' useful information in a multi-regional context. Concerning the forecasting phases, our analysis highlights the necessity of carrying out further research experiments - by introducing additional economic background variables - in order to get more insight into the mechanism and structure of spatio-temporal employment data.
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Paper provided by European Regional Science Association in its series ERSA conference papers with number
ersa02p117.
Length: Date of creation: Aug 2002 Date of revision: Handle: RePEc:wiw:wiwrsa:ersa02p117
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David G. Blanchflower & Andrew J. Oswald, 1990.
"The Wage Curve,"
NBER Working Papers
3181, National Bureau of Economic Research, Inc.
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Blanchflower, D. & Oswald, A., 1989.
"The Wage Curve,"
Papers
340, London School of Economics - Centre for Labour Economics.
Kennedy, S. & Borland, J., 1997.
"A wage Curve for Australia?,"
Discussion Papers
372, Centre for Economic Policy Research, Research School of Social Sciences, Australian National University.
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