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Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model

Author

Listed:
  • 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)

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.

Suggested Citation

  • Man Wang & Kun Chen & Qin Luo & Chao Cheng, 2018. "Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model," Sustainability, MDPI, vol. 10(6), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1691-:d:148435
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    References listed on IDEAS

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