Inflation Forecasting Using Support Vector Regression

Inflation forecasting plays an important role in monetary policy and daily life. This study focuses on developing an inflation support vector regression (SVR) model to forecast CPI. Money gap and CPI historical data are utilized to perform forecasts. Furthermore, grid search method is applied to select the parameters of SVR. In addition, this study examines the feasibility of applying SVR in inflation forecasting by comparing it with back-propagation neural network and linear regression. The result shows SVR provides a promising alternative to inflation prediction.