LAUSR

Forecasting inflation rate is one of the most important topics in finance and economics. In recent years, China has stepped into a “New Normal” stage of economic development, with a different state from the fast growth period during the past few decades. Hence, forecasting the inflation rate of China with a time-varying model may give high accuracy. In this paper, we investigate the problem of forecasting the inflation rate with a functional coefficient autoregressive (FAR) model, which allows the coefficient to change over time. We compare the FAR model based on the B-splines estimation method with the autoregressive moving average (ARMA) model by extensive simulation studies. In addition, with the monthly CPI data of China, we conduct both in-sample analysis and out-of-sample forecasting. The forecasting result shows that the FAR model based on the B-splines estimation method has a better performance than the ARMA model.