Use of machine learning and multilevel analysis in hierarchical approaches of public expenditure forecasting | IEEE Conference Publication | IEEE Xplore

Use of machine learning and multilevel analysis in hierarchical approaches of public expenditure forecasting


Abstract:

On the one hand, public expenditure can be decomposed into several levels of expenditure regarding each administrative unit and expenditure nature groups that happens thr...Show More

Abstract:

On the one hand, public expenditure can be decomposed into several levels of expenditure regarding each administrative unit and expenditure nature groups that happens thru time. Therefore, it can be considered as a hierarchical time series. On the other hand, accurate forecasting methods are desirable for planning and management due to the need to identify and anticipate future scenarios. Thus, considering the hierarchical nature of the problem, this work aims to develop a a predictive model which takes into consideration the hierarchical structure of public expenditure in order to assure financial coherence and achieve improved results. Experimentally, this work uses time series of public expenditure execution in the State of Pernambuco, Brazil. The experiments were conducted on two axes in order to establish a multilevel analysis considering conciliatory approaches of the hierarchical levels of expenditure and the predictive models. By applying these methods, valuable public expenditure forecasts in the State of Pernambuco considering different hierarchical levels were produced.
Date of Conference: 09-12 October 2022
Date Added to IEEE Xplore: 18 November 2022
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Conference Location: Prague, Czech Republic

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