中国季度GDP的即时预测与混频分析  

Nowcasting China’s Quarterly GDP Using Mixed-Frequency Data

作　　者：王霞 司诺 宋涛[3] WANG Xia;SI Nuo;SONG Tao(School of Economics,Renmin University of China;Lingnan College,Sun Yat-sen University;School of Economics,Xiamen University)

机构地区：[1]中国人民大学经济学院,北京100872 [2]中山大学岭南学院,广东广州510275 [3]厦门大学经济学院,福建厦门361005

出　　处：《金融研究》2021年第8期22-41,共20页Journal of Financial Research

基　　金：国家自然科学基金面上项目“非线性因子模型:估计、检验与应用”(项目编号:71873151);国家社科基金重大项目“增强消费对经济发展的基础性作用研究”(项目编号:21ZDA036)的资助;北京高校“双一流”建设资金支持。

摘　　要：及时、准确地获得GDP短期预测值对于宏观调控和企业决策至关重要。本文在收集我国实时碎尾数据集的基础上,采用混频动态因子模型,将我国季度GDP的预测频率由"季度"提高到"日度"。研究结果表明,相对于混频抽样模型以及MFVAR等现有模型,混频动态因子模型能够有效解决实时预测中需要面临的数据问题,包括混频指标、碎尾特征、数据的周期性缺失等。本文模型在每个数据发布日,均可更新GDP的预测结果,这不仅将最新的经济活动信息迅速地体现到GDP预测中,而且显著提高了GDP即时预测的准确性,且预测结果随着月度数据信息的增加趋近于GDP真实值。此外,本文还估算了拟GDP季度同比增长率和GDP月度同比增长率两个月度数据序列,为我国宏观经济监测与政策分析提供一定的数据支撑。As GDP can comprehensively reflect the economic condition of a country or a region,GDP predictions are carefully scrutinized by many institutions.However,because GDP is usually only calculated on a quarterly frequency and released after a delay of 3 weeks,classical forecasting models cannot provide accurate and timely GDP predictions.However,some macroeconomic variables that are highly correlated with GDP,such as industrial added value,import and export volumes,and the total retail sales of consumer goods,are released monthly with a much smaller delay.The incorporation of this monthly information into GDP prediction could therefore improve the timeliness of GDP forecasting,enable the correct evaluation of economic conditions,and facilitate the formulation of appropriate macroeconomic regulations.However,the incorporation of these monthly indicators into economic forecasting models will require the solution of key problems associated with these indicators’underlying data,namely its mixture of data frequencies,the ragged-edge behavior of real-time data,the presence of data revision,and the periodic absence of data.To deal with the problems of absent data and ragged-edge data,we nowcast China’s GDP based on Zheng and Wang’s(2012,2013)mixed-frequency dynamic factor model for year-on-year growth rates.Compared with mixed data sampling(MIDAS)and mixed-frequency vector autoregression(MFVAR)models,the mixed-frequency dynamic factor model accounts for missing data in addition to dealing with ragged-edge data,and thus makes full,accurate,and timely use of the data.In addition,a year-on-year growth rate model is more useful in China,as the National Bureau of Statistics announces only year-on-year growth rates for most macroeconomic indicators,and policymakers focus on year-on-year GDP growth rates.Moreover,as year-on-year growth rates are based on data for the same month or quarter each year,they can mitigate effects due to seasonality,which is not generally accounted for in official year-on-year economic growth data

关 键 词：GDP 即时预测 混频因子模型 

分 类 号：F124[经济管理—世界经济]