基于工业电力大数据的GDP数据精准测算实证分析

Empirical Analysis of Accurate Measurement of GDP Data Based on Industrial Power Big Data

国家政策的制定与实施通常将GDP数据作为主要参考依据,但GDP数据存在时滞性和频率低等弊端,往往导致政策的制定与出台有着明显的滞后性且针对性不强等。为使政策制定及时高效,以江苏省工业电力大数据为例,通过构建动态倒向回归方程并在方程构建中纳入其影响因素,用渐次回归法并设定窗口期来实现GDP数据波动的动态调整及精准测算,此外还根据2007—2018年江苏省电力大数据进行回测,验证了该方法的有效性,且发现其能在一定程度上消除GDP数据的滞后和低频问题。

National policy formulation and implementation usually use GDP data as the main reference point,however,the drawbacks of GDP data,such as time lag and low frequency,often lead to the formulation and introduction of policies with obvious lag and lack of specificity.This study,taking the industrial power big data of Jiangsu Province as an example,constructs the dynamic backward regression equation and incorporates the affected factors in the equation construction.It uses the progressive regression method and sets the window period to realize the dynamic adjustment and accurate measurement of GDP data fluctuation.In addition,according to the power generation of Jiangsu province in 2007-2018,the data is back tested to verify the effectiveness of the method,and it is found that it can eliminate the lag and low frequency problems of GDP data to some extent.