Forecasting public expenditure by using feed-forward neural networks

In this paper, we analyse the correlation of the public expenses by functions with real GDP growth, elaborating a model of estimating and forecasting the main public expenses in some selected Central and Eastern European (CEE) countries: Hungary, Poland, the Czech Republic, Bulgaria and Romania. These countries have not adopted the euro yet. This paper presents several forecasting models for the CEE countries public expenditures, during 2015–2016. The models offer a base for the analysis of the potential budgetary implications of the government policies for the target countries. A short- and mid-term forecast for public expenditure is an important part of the modern methods of governmental management for the Central and Eastern European countries. This involves taking into account a wide range of factors, from GDP, inflation, demographic evolution and age share, to public expenditure type correlation. Such a forecast can be obtained with the help of artificial neural networks (ANNs), using the application GMDH Shell, which proved its ability to create complex and accurate forecasts for the economic, social and financial domains.