Forecasting China's GDP growth using dynamic factors and mixed-frequency data |
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| Institution: | 1. Quantitative Risk Analysis Section, Federal Reserve Board, Washington DC 20551, United States;2. Department of Economics, Duke University, Box 90097, Durham NC 27708, United States;1. Vrije Universiteit Amsterdam, Netherlands;2. Tinbergen Institute Amsterdam, Netherlands;3. CREATES, Aarhus University, Denmark;1. Department of Economics and Department of Finance, Kenan-Flagler Business School, University of North Carolina at Chapel Hill, United States;2. Department of Economics, UNC Chapel Hill, United States;3. Faculty of Political Science and Economics, Waseda University, Japan;1. Kenan-Flagler Business School, Chapel Hill, NC, United States;2. Department of Economics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States;1. Deutsche Bundesbank, Macroeconomic Analysis and Projection Division, Germany;2. Deutsche Bundesbank, General Economic Statistics Division, Germany |
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| 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. |
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| Keywords: | GDP growth forecast Dynamic factor model MIDAS regression |
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