Statistically predicting cash receipts and payments, an applied study using Box-Jenks models and the regression method

Abstract

nstitutions and economic units seek to adopt statistical methods in planning and forecasting to help decision makers confront future problems. This study aimed to highlight the importance of the ability to predict cash receipts and payments using statistical methods, and to choose the best and most appropriate one from the data previously available for a selected economic unit, using advanced statistical programs such as MINITAB and SPSS for successive financial periods, as estimated by the personal method and taking into account the last year. Year comparison.

Statistical analysis was done using the time series method (Box-Jinks), regression method, and analysis of variance (ANOVA) tables. Therefore, the choice of the statistical method and the choice of the forecasting method depend on the quantity and accuracy of the data, and the different statistical methods are not necessarily in conflict, but rather they often support each other. The study concluded:

The regression method is considered more accurate in statistically predicting accounting variables for a future period, and the Box and Jenkins method is more accurate for long periods