A Review of Industrial Economic Structure and Efficiency from a Statistical Perspective

This paper provides a comprehensive review of industrial economic structure and efficiency through the lens of statistical methodologies. It systematically discusses key concepts such as technical, allocative, and scale efficiency, and critically evaluates widely used measurement techniques including Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The paper further explores a broad spectrum of statistical tools — such as descriptive statistics, multivariate analysis, time series and panel data models, as well as spatial statistics and geographically weighted regression — and their applications in analyzing industrial dynamics. Empirical studies illustrating variations across different industries and regions are summarized, highlighting the importance of data quality and processing methods. The integration of traditional statistical approaches with emerging machine learning techniques is also examined, pointing toward future research directions. Finally, the study reflects on the practical implications for policy and industrial development, emphasizing both the strengths and limitations of statistical perspectives in industrial economics research.