Advances in Time Series Forecasting

Volume: 1

Public Expenditure Forecast by Using Feed Forward Neural Networks

Author(s): Alparslan A. Basaran, Cagdas Hakan Aladag, Necmiddin Bagdadioglu and Suleyman Gunay

Pp: 40-47 (8)

DOI: 10.2174/978160805373511201010040

* (Excluding Mailing and Handling)


The accurate forecast of public expenditure is crucial for the success of the new public financial management approach developed in Turkey since the financial crisis of 2001. The public institutions are now obliged to align their expenditure with the framework shaped by the Public Financial Management and Control Law (No: 5018), the Middle-Term Programme of 2010-2012, and recently the Fiscal Rule envisaged to apply in the next budgetary period. This necessitates a better forecasting method than the traditional way of budget forecasting, which is typically based on the expenditures of previous years adjusted by inflation. Particularly focusing on the expenditure side of the budget, this chapter applies various artificial neural networks models to the expenditures of 1973-2008 of two Turkish public institutions, namely, the State Planning Organization and the Court of Accounts to achieve accurate forecast levels. The artificial neural networks approach is rarely applied for the forecasting of public expenditures, and as far as we know this is the first of such attempts involving Turkish data. The artificial neural networks application provided very accurate public expenditure forecasts for these public institutions, suggesting that the artificial neural networks is a very useful method for the public expenditure forecasting, as well.

Keywords: Artificial neural networks, Budget forecasting, Public expenditure, Time series

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