A New Application of the Support Vector Regression on the Construction of Financial Conditions Index to CPI Prediction

Abstract

A regression model based on Support Vector Machine is used in constructing Financial Conditions Index (FCI) to explore the link between composite index of financial indicators and future inflation. Compared with the traditional econometric method, our model takes the advantage of the machine learning method to give a more accurate forecast of future CPI in small dataset. In addition, we add more financial indicators including M2 growth rate, growth rate of housing sales and lag CPI in our model which is more in line with economy. A monthly data of Chinese CPI and other financial indicators are adopted to construct FCI (SVRs) with different lag terms. The experiment result shows that FCI (SVRs) performs better than VAR impulse response analysis. As a result, our model based on support vector regression in construction of FCI is appropriate.