Regional unemployment forecasting using structural-component models with spatial autocorrelation

Abstract

"Labour-market policies in Germany are increasingly being decided on a regional level. This implies that institutions have an increased need for regional forecasts as a guideline for their decision-making process. Therefore, we forecast regional unemployment in the 176 German labour-market districts. We use an augmented structural-component (SC) model and compare the results from this model with those from basic SC and autoregressive integrated moving average (ARIMA) models. Basic SC models lack two important dimensions: First, they only use level, trend, seasonal and cyclical components, although former periods of the dependent variable generally have a significant influence on the current value. Second, as spatial units become smaller, the interdependence between them increases. In this paper we augment the SC model for structural breaks, autoregressive components and spatial lags. Using unemployment data from the Federal Employment Services in Germany for the period December 1997 to December 2005, we first estimate basic SC models with components for structural breaks and ARIMA models for each spatial unit separately. In a second stage, autoregressive components are added into the SC model. Third, spatial autocorrelation is introduced into the SC model. We test the quality of the models with simulated out-of-sample forecasts for the period January 2005 to December 2005. Our results show that the SC model with autoregressive elements is not superior to basic SC and ARIMA models in most of the German labour-market districts. The SC model with spatial autocorrelation performs better than the other models in labour-market districts which have a low seasonal span and a relatively high unemployment rate." (Author's abstract, IAB-Doku) ((en))

Cite article

Hampel, K., Kunz, M., Schanne, N., Wapler, R. & Weyh, A. (2006): Regional unemployment forecasting using structural-component models with spatial autocorrelation. Paper submitted to the Annual Conference of the European Regional Science Association (ERSA). 34 p.