Research on Economic Periodicity Based on Principal Component-Weighted Distance and Clustering Analysis

Qingdao No.2 Middle School Qingdao, China

^a 2648717728@qq.com
^b puq2002@163.com
^c 1481239253@qq.com

Abstract

Based on the knowledge of economics, this paper selects 22 macroeconomic indicators that best reflect the overall economic situation of the United States. After differential, logarithmic and exponential preprocessing of the original data, this paper, based on the power spectral analysis model, adaptively identifies the periodicity of the selected economic indicators, and visualize the results. As a result, it screens out 11 indicators with obvious periodicity. In the process of solving the weighted distance based on principal component analysis, correlation test is first conducted on the selected 11 single indicators of periodicity to obtain Pearson correlation heatmap. Then, the principal components are extracted by selecting the first five principal components as the virtual indicators to represent the monthly economic situation, and calculating the weighted distance value between months for visualization. Finally, we select the results of 36 months’ smoothing for analysis, figure out the time intervals with similar economic situation, and verify the conjecture of economic periodicity.

Finally, based on K-MEAN clustering analysis, the economic conditions of 352 months are classified into 3 clusters by using the weighted distance after 36 months’ smoothing. From the visualized results, it is found that there are two complete cycles, i.e. red-yellow-blue and red-yellow-blue, which is consistent with the conclusion of principal component analysis model, and proves the existence of economic cycle again.

In conclusion, based on the above PCA weighted distance and clustering analysis, it can be concluded that the economic period is around 176 months, in favor of medium long periodicity theory.