State debt assessment and forecasting: time series analysis
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DOIhttp://dx.doi.org/10.21511/imfi.18(1).2021.06
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Article InfoVolume 18 2021, Issue #1, pp. 65-75
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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.
- Keywords
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JEL Classification (Paper profile tab)H60, H63
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References29
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Tables1
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Figures7
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- Figure 1. Time series graph of the State Debt of Ukraine 2004–2020, UAH mln
- Figure 2. Monthly growth rates of Ukraine’s state debt for 2004–2020, %
- Figure 3. Annual growth rates of Ukraine’s State Debt for 2004–2020, %
- Figure 4. Time series graph of the State Debt of Ukraine 2004–2020, USD ths.
- Figure 5. The State Debt of Ukraine 2004–2020 сorrelogram, USD ths.
- Figure 6. Сorrelogram of the State Debt of Ukraine 2004–2020 in first differences
- Figure 7. Forecasted amount of Ukraine’s state debt, USD bln
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- Table 1. Results of testing time series for stationarity in first differences
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- Bogdan, T. P. (2013) Derzhavnyi borh Ukrainy: osoblyvosti formuvannia ta upravlinnia v suchasnykh umovakh [State debt of Ukraine: features of formation and management in modern conditions]. Finansy Ukrainy – Finance of Ukraine, 1, 32-46.
- Box, G., Jenkins, G. M., & Reinsel G. (1994). Time Series Analysis: Forecasting & Control (3rd ed.) (614 p.). Prentice Hall.
- Correia, L., & Martins, P. (2019). The European crisis: Analysis of the macroeconomic imbalances in the rescued euro area countries. Journal of International Studies, 12(2), 22-45.
- D’yakonova, I., Nikitina, A., Sukhonos, V., & Zhuravka F. (2018). Methodological bases of estimating the efficiency of economic security management of the enterprises in the global environment. Investment Management and Financial Innovations, 15(2), 145-153.
- Galiński, P. (2015). Determinants of Debt Limits in Local Governments: Case of Poland. Procedia – Social and Behavioral Sciences, 213, 376-382.
- Gnegne, Y., & Jawadi, F. (2013). Boundedness and nonlinearities in state debt dynamics: A TAR assessment. Economic Modelling, 34, 154-160.
- Goswami, G. G., & Hossain, M. M. (2013). From Judgmental Projection to Time Series Forecast: Does it Alter the Debt Sustainability Analysis of Bangladesh? The Bangladesh Development Studies, XXXVI(3).
- Greene, W. H. (2012). Econometrics analysis (pp. 325-335). New York University.
- Jashhenko, L. O. (2014). Prohnozuvannia derzhavnoho borhu na osnovi dokhodiv ta vydatkiv derzhavnoho bjudzhetu Ukrainy [State debt forecasting based on revenues and expenditures of the state budget of Ukraine]. Statystyka Ukrainy – Statistics of Ukraine, 2, 14-19.
- Kliestik, T., Valaskova, K., Lazaroiu, G., Kovacova, M., & Vrbka, J. (2020). Remaining Financially Healthy and Competitive: The Role of Financial Predictors. Journal of Competitiveness, 12(1), 74-92.
- Kondrat, I. Yu. (2011). Prognozuvannia pokaznykiv derzhavnoho borhu yak faktora ekonomichnoi bezpeky Ukrainy. Naukovyi visnyk NLTU Ukrainy – Scientific Bulletin of NLTU of Ukraine, 209-216.
- Luk’janenko, I. G., & Zhuk, V. M. (2013). Analiz chasovykh riadiv. Pobudova ARIMA, ARCH/GARCH modelei z vykorystanniam paketa E.Views [Time series analysis. Construction of ARIMA, ARCH/GARCH models using E.Views package] (187 р.). Kyiv: NaUKMA; Agrar Media Grup.
- Martinez, A. B. (2015). How good are US government forecasts of the federal debt? International Journal of Forecasting, 31(2), 312-324.
- Martynjuk, V. P. (2011). Prohnozuvannia nadkhodzhennia podatkovykh platezhiv do derzhavnoho biudzhetu za dopomohoiu vykorystannia ARIMA-modeli [Forecasting the receipt of tax payments to the state budget using the ARIMA model].
- Ministry of Finance of Ukraine. (n.d.). Statystychni materialy shchodo derzhavnoho ta harantovanoho derzhavoiu borhu Ukrainy [Statistical materials on the state-guaranteed debt of Ukraine].
- Navapan, K., & Boonyakunakorn, P. (2017). Forecasting the Growth of Total Debt Service Ratio with ARIMA and State Space Model. Predictive Econometrics and Big Data (Studies in Computational Intelligence), 753, 492-501.
- Nikoloski, A., & Nedanovski, P. (2017). State debt dynamics and possibilities for its projection. The case of the Republic of Macedonia (pp. 720-734). DIEM: Dubrovnik International Economic Meeting.
- Plastun, A., Kozmenko, S., Plastun V., & Filatova, H. (2019). Market anomalies and data persistence: The case of the day-of-the-week effect. Journal of International Studies, 12(3), 122-130.
- Reinhart, Carmen M., & Kenneth S. Rogoff. (2011). From Financial Crash to Debt Crisis. American Economic Review, 101(5), 1676-1706.
- Salnykova, T. V. (2017). Derzhavnyi borh Ukrainy: otsinka ta napriamy pidvyshchennia efektyvnosti upravlinnia. Ekonomichnyi visnyk DVNZ “Pereyaslav-Khmelnytszkyi derzhavnyi pedahohichnyi universytet imeni Hryhoriia Skovorody” – Economic Bulletin of the SHEI “Hryhorii Skovoroda Pereyaslav-Hkmelnytsky state pedagogical university, 33/1, 385-394.
- Slutsky, E. (1927). The summation of random causes as the source of cyclic processes. Econometrica, 5, 105-146.
- Snieška, V., & Burksaitiene, D. (2018). Panel data analysis of public and private debt and house price influence on GDP in the European Union countries. Engineering economics, 29(2), 197-204.
- Stawska, J. (2015). The public finance sector debt and economic growth in Poland in the context of financial crisis. Magnanimitas, 6, 570-578.
- Tiftik, E., & Mahmood, K. (2020). Global Debt Monitor COVID-19 Lights a Fuse. Institute of International Finance.
- Tsaruk, O. V. (2007). Statystychne prohnozuvannia derzhavnoho borhu Ukrainy na osnovi protsesiv Boksa-Dzhenkinsa [Statistical forecasting of Ukraine’s public debt based on Box-Jenkins processes]. Problemy statystyky – Problems of statistics, 1-8.
- Tung, L. T. (2020). Can public debt harm social development? Evidence from the Asian-Pacific region. Journal of International Studies, 13(2), 48-61.
- Ytkyna, A. Ya. (2015). Vremennyye riady. Modelirovaniye processov tipa ARIMA(p, d, q) [Time series. Modeling ARIMA (p, d, q)].
- Yule, G. U. (1927). On a method for investigating periodicities in disturbed series with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London, Series A 226, 267-298.
- Zhuravka, F., Filatova, H., & Aiyedogbon, J. O. (2019). Government debt forecasting based on the Arima model. Public and Municipal Finance, 8(1), 120-127.