A Multivariate-transmission-based New Approach for Forecasting China's Price Indexes in 2018

Abstract

Abstract:

Accurate forecasting of price indexes would provide solid support for effective macroeconomic policy decisions. Thus, one of the most significant issues in economic policy practice is to improve the forecasting performance of price indexes. In this paper, a new approach is proposed to forecast three main price indexes: Purchase Price Index for Industrial Products (PPIRM), Producer Price Index for Industrial Products (PPI) and Consumer Price Index (CPI). The proposed multivariate transmission method is based on the price transmission mechanism among the three price indexes by using the Granger causality test. According to this method, different econometric forecasting models are selected for different components of the three price indexes and the results are properly integrated. The empirical results indicate that the integrated model based on the multivariate transmission method outperforms the benchmark model in terms of both level and directional predictive accuracy. Furthermore, the method is used to forecast China's CPI, PPI and PPIRM in 2018. The results suggest that in 2018, CPI will modestly increase by 2.1 percent from 2017 with 1 percent caused by tail-raising factor, and PPI and PPIRM will increase by 3.6 percent and 4.3 percent respectively with 2.4 percent and 2.8 percent caused respectively by tail-raising factor.

Key words: PPIRM, PPI, CPI, inflation forecasting, multivariate transmission method, China's economy in 2018

Luo Xiaoqiang, Bao Qin, Wei Yunjie, Yang Boyu. A Multivariate-transmission-based New Approach for Forecasting China's Price Indexes in 2018[J]. , 2018, 30(1): 3-13.

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