Forecasting Regional Labour Market Developments Under Spatial Heterogeneity and Spatial Autocorrelation

Simonetta Longhi and Peter Nijkamp

No 05-041/3, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level. As a result, the need for economic forecasts at a sub-national level is rapidly increasing. The data available to compute regional forecasts is usually based on a pseudo-panel that consists of a limited number of observations over time, and a large number of areas (regions) strongly interacting with each other. In such a situation, the application of traditional time-series techniques to distinct time series of regional data may then become a sub-optimal forecasting strategy. In the field of regional forecasting of socio-economic variables, both linear and non-linear models have recently been applied and evaluated. However, often such analyses tend to ignore the spatial structure of the data and the spatial interactions that are likely to exist among regions. In this paper, we evaluate the ability of different statistical techniques – namely spatial lag and spatial error models – to correct for misspecification due to neglected spatial autocorrelation in the data set. Our empirical application concerns short-term forecasts of employment in 326 West German labour market regions. We find that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of non-spatial forecasting models.

Keywords: Space-Time Data; Regional Forecasts; Spatial Heterogeneity; Spatial Spillovers (search for similar items in EconPapers)
JEL-codes: C21 C23 E27 R19 (search for similar items in EconPapers)
Date: 2005-04-25
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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