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Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model

Man Wang (), Kun Chen (), Qin Luo () and Chao Cheng ()
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Man Wang: Department of Finance, Donghua University, Shanghai 200051, China
Kun Chen: School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
Qin Luo: Guangxi Xijiang Venure Investment Co. Ltd., Nanning 530022, China
Chao Cheng: Department of Mathmatical Sciences, Tsinghua University, Beijing 100084, China

Sustainability, 2018, vol. 10, issue 6, 1-16

Abstract: 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.

Keywords: B-splines; inflation forecast; monthly CPI data; out-of-sample forecast (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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