A novel spatial mixed frequency forecasting model with application to Chinese regional GDP

Xianning Wang (), Jingrong Dong, Zhi Xiao () and Guanjie He ()
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Xianning Wang: School of Economics and Management, Chongqing Normal University, Chongqing, China.
Jingrong Dong: School of Economics and Management, Chongqing Normal University, Chongqing, China.
Zhi Xiao: School of Economics and Business Administration, Chongqing University, Chongqing, China.
Guanjie He: School of Economics and Management, Chongqing Normal University, Chongqing, China.

Journal for Economic Forecasting, 2019, issue 2, 54-77

Abstract: Direct use of economic indicators for different frequencies is important to improve the regional forecast performance, and is the quantitative basis for improving the awareness on regional economic cycle changes, growth drivers and regional differences Considering the spatial mixed frequency data using a high frequency variable to predict a low frequency one in regional prediction problems, this paper proposes a novel spatial mixed frequency forecasting model. Firstly, it analyzes the commonly used spatial forecasting models and the most classical MIDAS. Secondly, it adopts the soft spatial weights to describe the spatial correlation of economic variables to amend the polynomial weighting method of MIDAS. Thirdly, it analyzes the main characteristics and puts forward the prediction error or precision index to test the validity of the model. Finally, it applies the new method to forecast the real GDP growth rate in 30 provinces and autonomous regions in China and compares the different weightings, which show a great feasibility. Futher, it discusses and gets some help findings about the characteristics of parameters such as weighs polynomials, prediction weighs, and lag period in different regions. Finally, it implements the Diebold Mariano tests for RMSEs between different model settings and obtains meaningful conclusions.

Keywords: Spatial mixed frequency; Forecastingl; MIDAS; Chinese regional GDP (search for similar items in EconPapers)
JEL-codes: C31 C53 E17 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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