Forecasting of population and economic variables in India using the Bayesian Structural Time Series (BSTS) Model
Author(s): Bheemanna and MN Megeri
Abstract: Bayesian Structural Time Series (BSTS) model is used to forecast time series. The goal of this study is to look forecasts of the population and economic variables in India by using the BSTS model for the next ten years. We have predicted the population of India's total, urban, and rural population, the GDP, and the age dependency ratio in this study. The research revealed that the projected results are very similar to the actual estimates. Due to the lowest MAPE values among the population and economic variables, the rural population has the best fit. The Age Dependency Ratio is well fitted compare to GDP in the economic variables, and the Rural Population is well fitted compared to other population variables. Test for bias shows population variables such as the Total and urban populations are underestimated and rural population is the overestimated and the economic variables are overestimated by using MALPE.
Bheemanna, MN Megeri. Forecasting of population and economic variables in India using the Bayesian Structural Time Series (BSTS) Model. Int J Stat Appl Math 2024;9(6):205-210. DOI: 10.22271/maths.2024.v9.i6c.1922
