Forecasting Budgetary Items in Türkiye Using Deep Learning

Altug Aydemir and Cem Cebi

Working Papers from Research and Monetary Policy Department, Central Bank of the Republic of Turkey

Abstract: 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.

Keywords: Machine Learning; Deep Learning; Artificial Neural Network (ANN); Deep Neural Network (DNN); Budget Forecast; Government Spending; Tax Revenue (search for similar items in EconPapers)
JEL-codes: C53 H20 H50 H68 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-ara
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.tcmb.gov.tr/wps/wcm/connect/a6270dd5-9 ... 6483a8c59634-pws1TAP (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tcb:wpaper:2509

Access Statistics for this paper

More papers in Working Papers from Research and Monetary Policy Department, Central Bank of the Republic of Turkey Contact information at EDIRC.
Bibliographic data for series maintained by Sermet Pekin () and Ilker Cakar () and ().