An Analytical Study on Forecasting GST Trends in Maharashtra and Karnataka Using Linear Regression and Monte Carlo Simulation

Abstract

Goods and Services tax (GST) was introduced in Indian economy in the year 2017 with the intention to build a simplified indirect tax regime stating as one nation one tax. It is a destination based and consumption-based tax to reduce the cascading effect on taxes and for subsuming several other taxes including VAT, Service tax, road tax etc. Further, the state of Maharashtra followed by Karnataka has been the highest in the collection (Singh, 2018)of GST revenue. This research paper analyses the trends in Goods and Services Tax (GST) collections for two Indian states, Maharashtra and Karnataka, by leveraging statistical modelling and computational simulations. The code provided serves as the analytical backbone for predicting future GST collections using linear regression and simulating ideal growth scenarios through Monte Carlo methods. This dual approach not only highlights the predicted growth patterns but also establishes an ideal growth trajectory within defined confidence intervals, thereby providing a benchmark for policy assessment and financial forecasting.