Government debt forecasting based on the Arima model

  • Received December 3, 2019;
    Accepted December 28, 2019;
    Published January 17, 2020
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  • Article Info
    Volume 8 2019, Issue #1, pp. 120-127
  • Cited by
    4 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The paper explores theoretical and practical aspects of forecasting the government debt in Ukraine. A visual analysis of changes in the amount of government debt was conducted, which has made it possible to conclude about the deepening of the debt crisis in the country. The autoregressive integrated moving average (ARIMA) is considered as the basic forecasting model; besides, the model work and its diagnostics are estimated. The EViews software package illustrates the procedure for forecasting the Ukrainian government debt for the ARIMA model: the series for stationarity was tested, the time series of monthly government debt was converted into stationary by making a number of transformations and determining model parameters; as a result, the most optimal specification for the ARIMA model was chosen.
Based on the simulated time series, it is concluded that ARIMA tools can be used to predict the government debt values.

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    • Figure 1. The schedule of the time series of the Ukraine’s government debt for 2011–2019, UAH mln
    • Figure 2. The results of the Dickey-Fuller test to verify the time series in the stationarity equations
    • Figure 3. The results of the time series check in the first differences for stationarity
    • Figure 4. The correlogram graph of the series in the first differences
    • Figure 5. Properties of ACF and PACF functions for MA, AR, and ARMA processes
    • Table 1. Test results for ARMA (p, q)
    • Table 2. Forecast of the government debt amount in Ukraine, UAH mln