Extrapolative techniques’ predictive capacity in the spatial downscaling of the Hungarian gross domestic product

Abstract

The dynamics of GDP (gross domestic product) and its spatial distribution are constantly at the forefront of economic and regional studies. This study intends to understand better Hungarian subnational economic processes by checking the predictive capacity of various extrapolative forecasting techniques with out-of-sample testing. The author focuses on a top-down projection method that allocates regional GDP based on an existing, external, national-level, long-term projection. GDP is analysed in its aggregate value and in a decomposition followed in the growth accounting literature. The main question of the out-of-sample tests is the level of usefulness of historical nationaland regional-level data in predicting Hungarian regional-level GDP in the long run. The author proposes a specific weighting scheme that combines past regional-level growth rates and predicted national-level growth rates to arrive at different regional-level predictions. She concludes that during the relatively short test period, the average historical inter-regional distribution did not have a determining role in predicting future regional GDP values. The nationallevel growth processes largely explain regional-level GDP, and the use of certain combinations of past regional-level growth rates and predicted national-level growth rates are recommended to project regional GDP in the long run.