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    数字经济时代宏观经济指标的实时化高频化与宏观现时预测(2021)

    摘要

    在大量经济活动数据变得实时可得的大数据时代,我国应该尽快建立基于大数据的实时高频宏观经济指标监测体系,从而更好地实时监测与预判宏观经济走势,这对于减少政策时滞、稳定市场预期和企业信心均有重要意义。建立实时高频宏观经济指标需要综合利用传统统计数据和各类新兴数据来源,尤其是各种实时大数据。在技术路线上,可以通过现时预测模型来建立实时高频宏观指标,模型搭建过程中需要处理好混频问题、高维问题、结构化模型与非结构化模型的配合利用问题等。在当前背景下,我国建立基于大数据的实时高频宏观经济指标监测体系的时机已经成熟且任务迫切,国家应该高度重视,尽快推进;建立数据共享平台,切实解决”数据孤岛“问题;建设实时高频宏观经济指标监测体系及相应实现系统,统一领导,多方协作。

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    Abstract

    In the era of big data when large amount of economic activity data becomes available in real time,China shall,in the short run,establish a real-time and high-frequency macroeconomic indicator monitoring system based on big data,so as to better monitor and predict macroeconomic trends in real time,which is highly important for reducing policy time-lag,stabilizing market expectation and enhancing corporate confidence. To establish a real-time and high-frequency macroeconomic indicators,the comprehensive use of traditional statistical data and various emerging data sources,especially various real-time big data,becomes crucial. Regarding the technical route,it is possible to establish real-time and high-frequency macro indicators by way of the current prediction model. And three key issues,including mixed-frequency problem,high-dimension problem,and the coordination and utilization of structured and unstructured models,need to be well addressed in the process of model building. In China’s context,it is the right time and there is an urgent need to establish a real-time and high-frequency macroeconomic indicator monitoring system based on big data. The Central Government is advised to:attach great importance to this endeavor and advance it in a swift manner;establish a data sharing platform to effectively solve the problem of “data” islands;construct a real-time and high-frequency macroeconomic indicator monitoring system and corresponding implementation system;take a unified leadership and encourage multiple parties’ engagement.

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    作者简介
    刘涛雄:刘涛雄,清华大学社会科学学院经济学研究所教授,主要研究方向为经济增长理论、宏观经济学、政治与经济、国防经济学。
    周晓磊:周晓磊,清华大学社会科学学院经济学研究所博士生,主要研究方向为深度学习和经济预测。
    姜婷凤:姜婷凤,对外经济贸易大学金融学院金融系讲师,主要研究方向为大数据与宏观经济、金融科技。
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