摘要
作为反映各部门间关系的平衡表,投入产出表是投入产出分析的重要数据基础。其中的中间投入为观测经济循环、制定宏观及产业政策提供有力支撑。然而,投入产出表的编制对基础数据条件要求较高,难以提高更新频率。受国内生产总值(GDP)即时预测(Giannone等,2008)的启发,本文利用大量相对高频的宏观及行业经济指标对中间投入进行即时有效的估计和预测。首先,构造了由2000余个序列组成的高维宏观经济指标集;其次,提出了基于自适应稀疏主成分分析的高维动态因子模型;再次,对各部门中间投入进行季度估算,并对2018年和2019年相关数据进行估测。研究结果表明,本文构建的高维模型可估算得到季度中间投入数据,其拟合和预测效果均优于传统时间序列模型和传统动态因子模型(Giannone等,2008)。此外,还对增频后估算的中间投入、中间投入率和中间投入贡献系数进行了分析。最后,本文基于即时预测的方法,尝试构建投入产出表中的中间流量矩阵,验证了本文模型在进行投入产出表预测时的可行性与稳定性。
As a balance table reflecting the relationship between various departments,the input-output table is an important data basis for input-output analysis.The intermediate input provides strong support to observe economic cycles and formulate macroeconomic and industrial policies.However,the compilation of input-output tables requires high standards of basic data,and it is difficult to increase the update frequency.Inspired by GDP Nowcasting(Giannone et al.,2008),we use a large number of high-frequency macro and industrial economic indicators to estimate and forecast the intermediate input immediately and effectively.Firstly,we construct a high-dimensional macroeconomic indicator set composed of more than 2000 sequences.Then we propose a high-dimensional dynamic factor model based on auto-adaptive sparse principal component analysis.Next,we estimate the quarterly intermediate inputs of various departments and forecast the relevant data for 2018 and 2019.The research results show that the high-dimensional model constructed in this paper can estimate the quarterly intermediate input data,and its fitting and forecasting performance are better than the traditional time series model and traditional dynamic factor model(Giannone et al.,2008).In addition,we also analyze the estimated intermediate input,intermediate input rate and intermediate input contribution coefficient after frequency increase.Finally,we attempt to construct the intermediate flow matrix in the input-output table based on the method of Nowcasting,which verifies the feasibility and stability of the high-dimensional dynamic factor model in forecasting the input-output table.
作者
杨翰方
李一繁
王祎帆
Yang Hanfang;Li Yifan;Wang Yifan
出处
《统计研究》
CSSCI
北大核心
2022年第6期17-35,共19页
Statistical Research
基金
中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目(21XNH156)。
关键词
中间投入
投入产出表
即时预测
高维动态因子模型
自适应稀疏主成分分析
Intermediate Input
Input-output Table
Nowcasting
High-dimensional Dynamic Factor Model
Auto-adaptive Sparse Principal Component Analysis
作者简介
杨翰方,中国人民大学应用统计科学研究中心研究员,中国人民大学统计学院副教授、博士生导师。研究方向为经济统计、数据科学;李一繁,中国人民大学统计学院硕士研究生。研究方向为经济统计;通讯作者:王祎帆,中国人民大学统计学院博士研究生。研究方向为宏观经济学、国际金融。电子邮箱:wyf0715@ruc.edu.cn。
