The development of a government cash forecasting model

Iskandar, Iskandar; Willett, Roger; Xu, Shuxiang

doi:10.1108/jpbafm-04-2018-0036

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…The ARIMA model combines the AR and MA models in the following equations (Jadevicius & Huston, 2015;Stevenson, 2007;Al-Shiab, 2006;Iskandar, et al, 2018;Balli & Mousa Elsamadisy, 2012):…”

Section: Methodsmentioning

confidence: 99%

Dynamic Forecasting of Government Foreign Debt: Case of Indonesia

Rahman

Pujiati

2021

jejak

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This study aims to forecast the value of the Indonesian government foreign debt in 2020-2024. The secondary data of time series during the period of 2010-2019 on Indonesian government foreign debt are used as the basis of forecasting for the next five years by using ARIMA (Autogressive Integrated Moving Average). The results show that the selected ARIMA models for forecasting are ARIMA (3,1,3) after the unit root test is carried out and 16 ARIMA models are tested. The value of government foreign debt is predicted to keep increasing from 2020 to 2024 amounted to USD 253.01. Then, compared to government debt in January 2010, within 11 years, government foreignÂ debt is predicted to rise by 169.6%.

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“…The ARIMA model combines the AR and MA models in the following equations (Jadevicius & Huston, 2015;Stevenson, 2007;Al-Shiab, 2006;Iskandar, et al, 2018;Balli & Mousa Elsamadisy, 2012):…”

Section: Methodsmentioning

confidence: 99%

Dynamic Forecasting of Government Foreign Debt: Case of Indonesia

Rahman

Pujiati

2021

jejak

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“…In studies on modeling and forecasting cash flows and reserves in the general government sector, three main traditional approaches are mainly used: statistical (for example, a linear method based on the ARMA/ARIMA/SARIMA econometric family) [23], machine learning (a nonlinear approach based on artificial intelligence and neural networks) [24], and a hybrid approach combining statistical and machine learning approaches [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In particular, forecasting the dynamics of a nonlinear and non-stationary time series is associated with some significant volatilityrelated problems due to the influence of seasonal and calendar factors and the mutual influence of various other factors (macroeconomic, social, political, technogenic, financial, market, etc.). Reasons are the complexity, unpredictability, and multi-scale nature of the environmental impact, which did not find due attention in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Karaev

Gorlova

Sedova

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Improving the accuracy of cash flow forecasting in the TSA is the key to fulfilling government payment obligations, minimizing the cost of maintaining the cash reserve, providing the absence of outstanding debt accumulation, and ensuring investment in various financial instruments to obtain additional income. The article describes a method for improving the accuracy of forecasting a time series composed of daily budgetary fund balances in the TSA, based on its preliminary decomposition using a discrete wavelet packet transform of the Daubechies family. This makes it possible to increase the accuracy of traditional forecasting methods from 80% to more than 96%. The decomposition level varied from one to eight to minimize the mean absolute error and improve the forecasting accuracy. Calculations of statistical tests for adequacy confirm the effectiveness of the proposed method for improving forecasting accuracy. The scientific novelty of the proposed method for improving the forecasting accuracy of time series from daily budgetary fund balances in the TSA lies in proving the need for preliminary timeseries decomposition and subsequent construction of forecasts for the obtained parts, resulting in high forecasting accuracy. The result differs significantly from traditional econometric methods (ARIMA/SARIMA), characterized by a much lower accuracy (50–80%) and a decrease in forecasting accuracy with an increase in the forecast horizon. This article is novel, as it forms a new approach to solving the problem of increasing the efficiency of using budgetary funds, associated with improving the accuracy of forecasting daily budgetary fund balance in the TSA.

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“…Kemampuan pemerintah Indonesia dalam membiayai setiap belanja negara merupakah hal penting dalam proses pelaksanaan anggaran negara, hal ini tidak lepas dalam memprediksi belanja atau uang tunai masa depan yang dibutuhkan untuk memenuhi tanggung jawab pemerintah dalam penyediaan layanan publik, serta Pada negara berkembang, khususnya Indonesia bahwa kebijakan pemerintah sangat berpengaruh terhadap besarnya tingkat pengeluaran belanjanya, sehingga variable kebijakan pemerintah merupakan salah satu faktor penyebab kenaikan proyeksi belanja [1].…”

Section: Pendahuluanunclassified

Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

Sautomo¹,

Pardede²

2021

RESTI

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Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model.

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The development of a government cash forecasting model

Cited by 6 publications

References 36 publications

Dynamic Forecasting of Government Foreign Debt: Case of Indonesia

Dynamic Forecasting of Government Foreign Debt: Case of Indonesia

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

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