Sequential Big Data-Based Macroeconomic Forecast for Industrial Value Added

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

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