Abstract
The existence of unobserved economy is one of the important factors affecting GDP calculation. This paper uses the provincial panel data from 2010 to 2019 in China, and adopts the method of principal component feature extraction to carry out cluster analysis on the multi-indicator panel data. This method preserves the dynamic characteristics of the panel data, calculates the comprehensive score of each eigenvalue, and gives weight to the eigenvalue by using the entropy method, so as to optimize the clustering results representing the eight indicators of the unobserved economy. Through the analysis, it is found that the regional development of China’s unobserved economy is obviously different, and each type has different influencing factors. This result has important practical significance for different regions in China to formulate differentiated unobserved economic governance policies. This also helps to make better use of resources and develop an energy-saving economy.
Highlights
Gross domestic product is a core indicator that reflects the economic development of a country
This paper uses the method of principal component feature extraction to process multi-indicator panel data by reading related literature, retains the dynamic characteristics of the panel data, calculates a comprehensive score for each feature, and uses the entropy method to perform systematic clustering, and analyzes the representative unobserved economy
Clustering the indicators of the unobserved economy, the weight of each principal component feature extracted by the entropy method is 0.239, 0.279, 0.168, 0.09, 0.224, respectively
Summary
Panel data (panel data) is called time series-crosssection data, which refers to the data obtained by the same cross-sectional units (such as households and companies) in different periods. It has the characteristics of time and space dimensions, and can reveal the dynamic characteristics of the research object. The basic idea of cluster analysis is to classify a batch of samples or variables according to their characteristics without prior knowledge. Multi-index panel data has many observations in the three dimensions of sample size, index, and time. Suppose there are q samples in the multi-index panel data, each sample has p indicators, and each individual recording time is T, each data point is used, where i=1,2,...,q; j=1,2, ..., p; t=1,2,...,T
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