The objective of this research is to analyze the time series of labor productivity in the
Commercial Bank of Syria for a period of ninety days. The pattern of change in
productivity is identified in order to construct a model that helps predict the
values of
productivity.
So we used Box Jenkins models in this study by using statistical methods Such as the
ADF, PP KPSS and Q stat tests to detect that the series is Non stationary, but when the first
difference was taken, the series becomes stationary, and confirmed by the same previous
tests.
A series of time series models were then filtered based on Autocorrelation (ACF) and
Partial Autocorrelation(PACF).
After selecting between several candidate models, by applying some statistical
methods such as MSE and BIC, we selected the best time series model ARIMA (1,1,1).
The significance of its coefficients was determined using t
