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Abstract

Purpose:Government cash forecasting is central to achieving effective government cash management but research in this area is scarce. The purpose of this paper is to address this shortcoming by developing a government cash forecasting model with an accuracy acceptable to the cash manager in emerging economies.Design/methodology/approach:The paper follows “top-down” approach to develop a government cash forecasting model. It uses the Indonesian Government expenditure data from 2008 to 2015 as an illustration. The study utilises ARIMA, neural network and hybrid models to investigate the best procedure for predicting government expenditure.Findings:The results show that the best method to build a government cash forecasting model is subject to forecasting performance measurement tool and the data used.Research limitations/implications:The study uses the data from one government only as its sample, which may limit the ability to generalise the results to a wider population.Originality/value:This paper is novel in developing a government cash forecasting model in the context of emerging economies.

Item Type:	Article
Authors/Creators:	Iskandar, I and Willett, R and Xu, S
Keywords:	neural networks, hybrid models, ARIMA model, government cash forecasting, public expenditure management
Journal or Publication Title:	Journal of Public Budgeting, Accounting & Financial Management
Publisher:	PrAcademics Press
ISSN:	1096-3367
DOI / ID Number:	https://doi.org/10.1108/JPBAFM-04-2018-0036
Copyright Information:	© 2018 Emerald Publishing Limited
Related URLs:	Google Scholar: Xu, S UTAS Profile: Xu, S
Item Statistics:	View statistics for this item

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