Measuring and Analyzing the Financial Sustainability of the Retirement and Social Security Fund in Iraq for the Period 2004-2018

Abstract

Time series analysis is one of the important topics in explaining the phenomena that occur during a certain period of time. The goal 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 tool 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, as it is considered one of the most important problems facing the fund due to the high numbers of out-of-service employees and the high numbers of retirees, which causes an increase in the size of retirement and social security fund expenditure and the fund's dependence on the revenues of subscriptions that It is almost completely relied upon in the fund’s financing. The characteristics of time series, co-integration testing, and the Vector Error Correction Model, and then the comparison through criteria, Sum Square Error Root Mean Square Error was used to find the best model of exponential preamble to predict future values, as it was found that the time series (expenditure and revenue) were static after taking The First Difference according to Augmented 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 there is a vector relationship from revenue to spending according to Vector Error Correction Model because a parameter Correcting the error limit is negative and significant and that the vector model for correcting the error between the expenditures and revenues of the fund is free from the problem of serial correlation and free from the problem of inconsistency of variance, and the best model for predicting the research variables is the introduction of the Holt's double exponential smoothing trend because it gave accurate results close to real values.