Suchergebnisse

"The labour-market policy-mix in Germany is increasingly being decided on
a regional level. This requires additional knowledge about the regional development
which (disaggregated) national forecasts cannot provide.
Therefore, we separately forecast employment for the 176 German labour-
market districts on a monthly basis. We first compare the prediction
accuracy of standard time-series methods: autoregressive integrated
moving averages (ARIMA), exponentially weighted moving averages
(EWMA) and the structural-components approach (SC) in these small spatial
units. Second, we augment the SC model by including autoregressive
elements (SCAR) in order to incorporate the influence of former periods of
the dependent variable on its current value. Due to the importance of spatial
interdependencies in small labour-market units, we further augment
the basic SC model by lagged values of neighbouring districts in a spatial
dynamic panel (SCSAR).
The prediction accuracies of the models are compared using the mean absolute
percentage forecast error (MAPFE) for the simulated out-of-sample
forecast for 2005. Our results show that the SCSAR is superior to the
SCAR and basic SC model. ARIMA and EWMA models perform slightly better
than SCSAR in many of the German labour-market districts. This reflects
that these two moving-average models can better capture the trend
reversal beginning in some regions at the end of 2004. All our models
have a high forecast quality with an average MAPFE lower than 2.2 percent." [authors abstract]