Inflation forecasting with Random Forest
The accuracy of inflation forecasts is, and has been, important for economic agents such as governments, central banks, companies, and the general public. Historically it has mainly been conducted with traditional statistical models that limits the usage of bigger datasets. This thesis will examine the performance of the machine learning model called Random Forest by forecasting Swedish inflation