Prediction of unemployment rate of Iran based on longitudinal model in Bayesian paradigm

The increasing difficulty of collecting data in traditional ways, due to the complexity of today's societies, necessitates the need to study to change or update statistical methods. Using other data sources and model-based techniques are some of the methods that can be used as alternatives to increase the accuracy of statistical estimates and inferences. Information sources from previous data (such as past censuses or recorded data) are always one of the most important sources for this purpose. Therefore, in this article for the first time in Iran's Statistical System, we predict the unemployment rate by using Bayesian inference and methods. Unemployment rate, as one of the most important socio-economic indicators of a country, has a great importance rule for micro and macroeconomic programing and policy making in national level.