Using Bayesian Inference and Prediction Methods to predict Iran's Unemployment Rate
The increasing challenges on collecting data by traditional ways have necessitated a study for the transformation or updating of survey methods due to the complexities of today's statistical populations. Using other data sources and modeling are some of the ways that can be employed as an alternative to the survey methods or to increase the accuracy of estimates and inferences from traditional surveys. Information sources derived from the past data (such as the past surveys or registered data) are always one of the most important sources of information for this purpose. Therefore, it seems that if the data from these data sources can be used in predicting or updating the estimates by Bayesian inference and modeling, a major step towards reducing the cost and error of the surveys, especially in official surveys will be taken. Accordingly, upon the request of the Statistical Centre of Iran on providing a method for estimating and predicting the unemployment rate obtained from the SCI’s Labour Force Survey, the Statistical Research and Training Centre, based on its mission on conducting research projects aimed at improving the quality of statistics, has put Bayesian Inference and Prediction Method to predict Iran's Unemployment Rate " in its working programme, whose final report is now available to the interested people.