Predicting the unemployment rate using autoregressive integrated moving average

The objective of this study is to predict unemployment in Indonesia in the wake of the demographic dividend. The sample used in this study is the unemployment data from 1990 to 2022 from the Indonesian Central Bureau of Statistics database. Using non-seasonal ARIMA (Autoregressive Integrated Moving Average) modeling, this study projected unemployment. It was predicted using six alternative models. With a mean absolute percent error (MAPE) of 9.56% (MAPE ≤10%), the predictions were quite accurate. It indicates that the ARIMA model has a good forecasting capability. According to the dynamic method’s unemployment projection, there will be less unemployment between 2023 and 2050. For Indonesia, maximizing the demographic dividend is both a challenge and an opportunity presented by the decline and stable number in unemployment. The demographic dividend will cause a substantial increase in employment and the creation of various new jobs. Several factors will support the demographic dividend. Thus, it could help governments to make decisions on labor issues. It also highlights a policymaker’s direction to pursue labor development, including employment trends.The purpose of this study is to forecast Indonesia’s unemployment rate following the demographic dividend. As a result, an Autoregressive Integrated Moving Average (ARIMA) was suggested for a univariate time-series analysis. Based on dynamic and static methodologies, my results demonstrate that the ARIMA model could predict Indonesia’s unemployment rate. The execution of sixteen economic policy packages, improvements to the human resources quality policy, and an accelerated infrastructure development strategy are some factors that promote the demographic dividend. A robust investigation shows that GDP has a positive and significant effect on the labor force between the ages of 15 and 19, as well as the influence of COVID-19 periods on unemployment in Indonesia. It could be helpful for government decision-making about labor issues. It also highlights the course that a policymaker wishes to pursue to achieve labor development.