Exploring Determinants of Inflation in China based on L1-∈-Twin Support Vector Regression

Abstract

As a novel feature selection approach, L1-norm E-twin support vector regression(L1-E- TSVR)is proposed in this paper to investigate determinants of cost-push inflation in China. Compared with L2-ε-TSVR, our L1-E- TSVR not only can fit function well, but also can do feature ranking. The computational results of inflation forecasts demonstrate that our L1-E- TSVR derives much smaller root mean squared error (RMSE) than the forecasts generated from ordinary least square (OLS) model. Furthermore, the feature selection results indicate that the most significant explanatory factor for the inflation in China is the housing sales price index. Therefore, the housing market do have an important impact on the inflation in China.

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