Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Yu Jiang, Yongji Guo and Yihao Zhang

Economic Modelling, 2017, vol. 66, issue C, 132-138

Abstract: Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.

Keywords: GDP growth forecast; Dynamic factor model; MIDAS regression (search for similar items in EconPapers)
JEL-codes: C53 E17 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:66:y:2017:i:c:p:132-138

DOI: 10.1016/j.econmod.2017.06.005

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