Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder

In this paper we forecast the annual growth rates of the real GDP for each of the 16 German Länder (States) simultaneously. To the best of our knowledge, this is the first attempt in the literature that addresses this question for all German Länder as most of the studies try to forecast the German GDP either on the aggregate level or focus on selected Länder only. Our further contribution to the literature is that next to the usual panel data models such as pooled and within models we apply within models that explicitly account for the spatially autocorrelated errors. On the one hand, it allows us to take advantage of the panel dimension, given the short sample for which the data are available, and hence gain efficiency and precision. On the other hand, accounting for the spatial heterogeneity and correlation is important due to the substantial differences existing between the German regions, in particular between East and West Germany. Our main finding is that pooling helps to significantly (up to 25% in terms of the root mean squared forecast errors) increase the forecasting accuracy compared to the individual autoregressive models estimated for each of the Länder separately.