قياس و تحليل الاستدامة المالية للاقتصاد العراقي للمدة ٢٠٠٤-٢٠١٨

Abstract EN

Time series analysis is one of the important topics in explaining the phenomena that occur during a certain period of time.

The aim of this analysis is to obtain a description and construction of a suitable model in order to give a clear future picture of the studied time series.

Time series is the most important tools used in building, estimating and predicting different phenomena.

Financial sustainability is the situation in which the state is able to fulfill its current and future obligations without changing its policies, the most important issues facing the state, especially the oil state, for its almost complete dependence on oil revenues, and this leads to fluctuation in the volume of public revenue (decrease and increase), which affects financial sustainability in addition To the high volume of public spending, as it used the characteristics of time series, the joint integration test, the Vector Error Correction Model, and then the comparison through the criteria, Sum Square Error Root Mean Square Error to find the best model of the exponential priming models to predict future values.

The time series (public spending and public revenue) is static after taking the first difference, according to Augm Ented Dickey Fuller, the research concluded that the optimal slowdown period is the third period, depending on the Akaike Information Criterion.

The Johansen - Juselius test indicated that there is a long-term equilibrium relationship between the variables and that there is a vector relationship from revenue to spending according to the Vector Error Correction Model because the error correction parameter Negative and moral and that the vector model for correcting the error between expenditures and revenues is free from the problem of serial correlation and free from the problem of inhomogeneity and the best model for predicting the research variables is the exponential introduction to my teacher because it gave accurate results close to real values.