Forecasting Budgetary Items in Türkiye Using Deep Learning

Forecasting Budgetary Items in Türkiye Using Deep Learning

25/09

Altuğ Aydemir, Cem Çebi

English

July 2025

This study aims at forecasting the future behavior of budget variables, using Artificial Neural Network (ANN) and Deep Neural Network (DNN) techniques for Türkiye. Particularly, we focus on budget expenditures, tax revenues and their main components. Annual data were used and divided into two sub-periods: a training set (2002-2019) and a test set (2020-2022). Each fiscal item is estimated using relevant explanatory variables selected based on economic theory. We achieved good forecasting performance for main budget items using ANN and DNN methodologies. We found that most of the Mean Absolute Error (MAE) values fell within the acceptable range, an indicator of good prediction performance. Second, we see that the MAE values for public expenditures are lower than taxes. Third, estimating total tax revenues (aggregate data) performs better compared to subcomponents of taxes (disaggregated data). The opposite is the case for public expenditures.

Machine learning, Deep learning, Artificial neural network (ANN), Deep neural network (DNN), Budget forecast, Government spending, Tax revenue

C53; H20; H50; H68