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Communications in Mathematics and Statistics

, Volume 7, Issue 4, pp 445–457 | Cite as

Sequential Big Data-Based Macroeconomic Forecast for Industrial Value Added

  • Yunli Yang
  • Jing Kong
  • Lu Yang
  • Zhouwang Yang
Article
  • 71 Downloads

Abstract

Macroeconomic situation is the overall performance of a country’s and regional economic situation. At present, the vast majority of macroeconomic indicators are obtained through sampling surveys, step-by-step reporting, statistical calculations, and other processes, which are publicly released by the Statistical Bureau. There are some shortcomings, such as lag and non-authenticity. Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs. However, the timeliness of data has a direct impact on government decision-making. In this paper, the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators (such as industrial value added above the scale of Hefei), which is different from the traditional time series prediction model such as ARIMA model. Based on the macroeconomic prediction model of time series big data, multi-latitude data sources, sequential update, verification set screening model and other strategies are used to provide more reliable, timely, and easy-to-understand forecasting values of national economic accounting indicators. At the same time, the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.

Keywords

Macroeconomics Time series big data Sequential update Multivariate regression prediction 

Mathematics Subject Classification

97M10 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their comments and suggestions which greatly improve the manuscript. The work is supported by the NSF of China (No. 11871447), and Anhui Initiative in Quantum Information Technologies (AHY150200).

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Copyright information

© School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yunli Yang
    • 1
  • Jing Kong
    • 1
  • Lu Yang
    • 1
  • Zhouwang Yang
    • 1
  1. 1.University of Science and Technology of ChinaHefeiChina

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