Indicator-Type Grey Structure Incidence Analysis Method for Panel Data and Its Application in Identifying Technological Innovation Factors

There exist many panel data decision problems in real life, and they take on obvious structural similarities and lag effects among decision objects or indicators, which are difficult to solve effectively based on traditional panel data analysis methods. To deal with these problems, considering the structural characteristics of panel data and lag effect, from multiple structural dimensions such as scale volume, development trend, and volatility, we exploit grey incidence analysis and panel data to establish an indicator-type grey structural incidence analysis model, and utilize it to analyze and identify factors influencing technological innovation of industrial enterprises. The results show that the proposed method fully considers the structural characteristics of panel data and lag effect, and it can deal with panel data decision problems and provide a new methodological support for the grey incidence analysis.