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The development of a government cash forecasting model

Datos Bibliográficos

ID20201489
AutoresIskandar Iskandar (0000-0001-6336-1741, University of Tasmania), Roger J Willett (0000-0003-2909-8462, Victoria University of Wellington), Roger Willett, Shuxiang Xu (0000-0003-0597-7040, University of Tasmania)
Año2018
Volumen30
Número4
Páginas368-383
Fecha de publicación2018-11-05
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Public Budgeting Accounting & Financial Management (JOURNAL)
Identificadores de la revistaISSN: 1096-3367 • E-ISSN: 1945-1814
EditorialEmerald (PUBLISHER)
DOI10.1108/jpbafm-04-2018-0036
OpenAlexW2893761861
IdiomaEN
Referencias citadas25

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

Autoregressive integrated moving average · Cash · Cash flow forecasting · Economics · Machine learning · Population · Time series · Computer Science · Fiscal Policies and Political Economy · Forecasting Techniques and Applications · Stock Market Forecasting Methods · Finance

  • Time series analysis

    George E P Box, Gregory C Reinsel et al.•Time series analysis•1970

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