comparative analysis of arima, sarimax, and random forest models for forecasting future gdp of the uk in relation to unemployment rate

Accurate forecasting of gross domestic product (gdp) is crucial for policymakers, businesses, and investors. this research explores the use of sarimax, arima, and random forest models to forecast gdp in the uk. the study investigates the relationship between gdp and the unemployment rate, considering historical gdp and unemployment data collected from the office of national statistics (ons). both sarimax and arima models indicate a negative relationship between gdp and the unemployment rate, although the coefficients are not statistically significant. on the other hand, the random forest model has shown its supremacy when it comes to the accuracy of prediction. the results suggest that other factors may have a stronger influence on gdp fluctuations based on the empirical findings. future research should consider additional variables and advanced modelling techniques to further explore the relationship between gdp and the unemployment rate, contributing to a deeper understanding of the uk economy and informing effective economic management.