Employment and unemployment figures represent key information from a macro- and microeconomic point of view. In particular, being able to forecast such values – in the short and long run – is a valuable asset when evaluating labour market policies. In particular, the recent econometric literature has pointed to the need for econometric techniques allowing to obtain local predictions, where the information on each spatial unit (e.g. a region) is considered together with the one on its neighbouring units, which are most likely to influence the spatial unit’s socioeconomic system with their own policies, due to spatial proximity. This book offers a view of how such aspects may be incorporated in a forecasting strategy. A number of techniques suited to obtain local predictions are presented. The proposed approaches range from shift-share analysis over dynamic panel data techniques to neural network applications. The volume concludes with a review of methods for evaluating competing forecasting approaches.

Forecasting Regional Labour Markets

PATUELLI, ROBERTO;
In corso di stampa

Abstract

Employment and unemployment figures represent key information from a macro- and microeconomic point of view. In particular, being able to forecast such values – in the short and long run – is a valuable asset when evaluating labour market policies. In particular, the recent econometric literature has pointed to the need for econometric techniques allowing to obtain local predictions, where the information on each spatial unit (e.g. a region) is considered together with the one on its neighbouring units, which are most likely to influence the spatial unit’s socioeconomic system with their own policies, due to spatial proximity. This book offers a view of how such aspects may be incorporated in a forecasting strategy. A number of techniques suited to obtain local predictions are presented. The proposed approaches range from shift-share analysis over dynamic panel data techniques to neural network applications. The volume concludes with a review of methods for evaluating competing forecasting approaches.
In corso di stampa
1
R. Patuelli; M. Mayor
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/170257
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