Data.MathematicalModelingEmployment.xlsx

Unemployment is one of the numerous socio-economic challenges that exist in all countries of the world. Accurate forecasting of the unemployment rate informs significant government and various stakeholders' decisions relating to fiscal and monetary policies, and investment, among others. The main objective of this study was to compare and examine the predictive accuracy of least square, Time series autoregressive distributed lag (ARDL) and Vector Autoregression (VAR) models for predicting the youth unemployment rate of Jordan. Using time series annual data from 1991 to 2021, the three models were fitted to model the youth unemployment rate of Jordan based on Population growth Rate (annual %), Current GDP in US$, Foreign direct investment, net (BoP, current US$), External debt stocks (% of GNI), Foreign direct investment, net inflows (% of GDP), Foreign direct investment, net outflows (% of GDP), Literacy rate, adult total (% of people ages 15 and above), School enrolment, tertiary (gross), gender parity index (GPI), and Inflation, consumer prices (annual %). The predictive accuracy of the models was assessed using mean absolute error, root mean square error and mean absolute percentage error.