Nowcasting of China’s industrial added value based on electric power big data

Fang PENG; Gaoqun PENG; Yaru QI; Tiantian LIU; Xiaolei ZHOU

doi:10.11959/j.issn.1000-0801.2021143

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Nowcasting of China’s industrial added value based on electric power big data

Research and Development | 更新时间：2024-06-05

- Nowcasting of China’s industrial added value based on electric power big data
- Telecommunications Science Vol. 37, Issue 7, Pages: 115-125(2021)
- 作者机构：
  
  1. 国家电网有限公司大数据中心，北京 100031
  2. 清华大学社会科学学院，北京 100084
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021143
  CLC： F224
- Published Online：2021-07，
  
  Published：20 July 2021
- 稿件说明：
移动端阅览
Fang PENG, Gaoqun PENG, Yaru QI, et al. Nowcasting of China’s industrial added value based on electric power big data[J]. Telecommunications science, 2021, 37(7): 115-125.
DOI：

Fang PENG, Gaoqun PENG, Yaru QI, et al. Nowcasting of China’s industrial added value based on electric power big data[J]. Telecommunications science, 2021, 37(7): 115-125. DOI： 10.11959/j.issn.1000-0801.2021143.

摘要

工业增加值是衡量实体经济运行状况的重要指标，为充分挖掘电力数据在宏观经济现时预测中的价值，服务于政府政策制定和经济社会发展，以电力数据和传统统计数据为基础，采用Bagging和Boosting等算法对工业增加值进行了现时预测。结果表明：第一，传统统计数据可以显著提升ARIMA模型的预测效果；第二，电力数据的预测效果取决于电力指标的选择，选择恰当的电力指标有助于更及时、准确地预测工业增加值；第三，电力数据信息对当期工业增加值的预测能力可能会低于对未来期工业增加值的预测能力，这意味着电力数据更适用于超前的预测。

Abstract

Industrial added value is an important indicator to measure the operation of the real economy.In order to fully mine the value of power data in the current macroeconomic nowcasting

so as to serve the government policy making

the Bagging and Boosting algorithms in machine learning were applied to nowcast industrial added value based on electric power data as well as traditional statistical data.Firstly

traditional statistical data can significantly improve the forecasting effect of the ARIMA model.Secondly

the nowcasting ability of electric power data depends on the selection of electric power indicators

and the proper electric power index is helpful to predict industrial added value more timely and accurately.Thirdly

the prediction ability of electric power data to the industrial added value in the current period may be lower than that in the future

which means the power data is more likely to be used to predict ahead of time.

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references

尹德才 , 张文 . 当前宏观经济现时预测研究综述 [J ] . 统计与信息论坛 , 2020 , 35 ( 1 ): 121 - 128 .

YIN D C , ZHANG W . A review of current macroeconomic nowcasting research [J ] . Statistics ＆ Information Forum , 2020 , 35 ( 1 ): 121 - 128 .

顾光同 , 许冰 . 中国工业增加值的半月预报:基于宏观月度数据 [J ] . 系统工程理论与实践 , 2018 , 38 ( 8 ): 1983 - 1993 .

GU G T , XU B . Half-month forecast of China’s industrial added value: based on the macro monthly data [J ] . Systems Engineer-ing-Theory ＆ Practice , 2018 , 38 ( 8 ): 1983 - 1993 .

王雷 , 聂常虹 . 中国债券利差对宏观经济指标的预测能力研究 [J ] . 管理评论 , 2019 , 31 ( 5 ): 66 - 76 .

WANG L , NIE C H . Research on how predictive Chinese bond spreads is of macroeconomic indices [J ] . Management Review , 2019 , 31 ( 5 ): 66 - 76 .

STOCK J H , WATSON M W . Forecasting using principal components from a large number of predictors [J ] . Journal of the American Statistical Association , 2002 , 97 ( 460 ): 1167 - 1179 .

STOCK H , WATSON W . Generalized shrinkage methods for forecasting using many predictors [J ] . Journal of Business and Economic Statistics , 2012 , 30 ( 4 ): 481 - 493 .

BAI J , NG S . Boosting diffusion indices [J ] . Journal of Applied Econometrics , 2009 , 24 ( 4 ): 607 - 629 .

BAI J , NG S . Determining the number of factors in approximate factor models [J ] . Econometrica , 2002 , 70 ( 1 ): 191 - 221 .

BREIMAN L . Bagging predictors [J ] . Machine Learning , 1996 , 24 ( 2 ): 123 - 140 .

INOUE A , KILIAN L . How useful is bagging in forecasting economic time series? A case study of U.S.consumer price inflation [J ] . Journal of the American Statistical Association , 2008 , 103 ( 482 ): 511 - 522 .

KIM H H , SWANSON N R . Forecasting financial and macroeconomic variables using data reduction methods:new empirical evidence [J ] . Journal of Econometrics , 2014 ( 178 ): 352 - 367 .

BÜHLMANN P , YU B . Boosting with the L 2 loss:regressi on and classification [J ] . Journal of the American Statistical Association , 2003 ( 98 ): 324 - 339 .

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