Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain

Abstract

We study the performance of different modelling strategies for 969 and 600 monthly price indexes disaggregated by sectors and geographical areas in Spain, regions, and in the EA12, countries, in order to obtain a detailed picture of inflation and relative sectoral prices through geographical areas for each economy, using the forecasts from those models. The study also provides a description of the spatial cointegration restrictions which could be useful for understanding price setting within an economy. We use spatial bi-dimensional vector equilibrium correction models, where the price indexes for each sector are allowed to be cointegrated with prices in neighbouring areas using different definitions of neighbourhood. We find that geographical disaggregation forecasts are very reliable on a regional level in Spain as they improve the forecasting accuracy of headline inflation relative to alternative methods. Geographical disaggregation forecasts are also reliable for the EA12 but only because derived headline inflation forecasting is not significantly worse than alternative forecasts. These results show that regional analysis within countries is appropriate in the euro area. These highly disaggregated forecasts can be used for competitive and other type of macro and regional analysis