Abstract

By use of monthly climate indicators and real GDP growth rate to build a dynamic mixed-frequency vector autoregressive (MFVAR) model, and by utilizing expectation maximum algorithm and Kalman filter, this paper realizes the estimation and iterative prediction on mixed data and missing data. The paper also employs MFVAR model with a large number of monthly climate indicators to do an iterative multi-step out-of-sample forecasting of pseudo real-time data. The result shows that the prediction result of different kinds of monthly climate variable is slightly different in different forecasting period, but the result of real-time forecast, short-term predication and combined forecasts all indicate that the dynamic MFVAR prediction model has accuracy, validity and applicability in real-time forecasting and short-term prediction on Chinese quarterly real GDP growth rate.

Key words

Mixed-frequency Data; MFVAR Model; GDP Growth Rate; Real-time Prediction; Short-term Forecasting

DOI: https://doi.org/10.13546/j.cnki.tjyjc.2017.21.006