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ABOUT PRINCIPAL COMPONENT ANALYSIS (PCA)
The text discusses Principal Component Analysis (PCA), focusing on its mathematical foundations, statistical interpretation, and geometric intuition. It emphasizes dimensionality reduction while preserving essential information. The essay also covers variable selection, optimization, and the relationship between PCA and linear regression. Additionally, historical context and practical applications of PCA in various fields are presented.
ON THE METHODOLOGY OF THE U.S. BUREAU OF ECONOMIC ANALYSIS FOR THE REDEFINITION AND REASSIGNMENT OF PRODUCTS IN THE U.S. INPUT-OUTPUT MATRIX
The document analyzes the methodological approach of the U.S. Bureau of Economic Analysis in dealing with secondary production within input-output matrices. It explains the redefinition and reallocation of outputs and inputs across industries, highlighting the importance of industry-technology and commodity-technology assumptions. Accurate interpretation of interindustry relationships hinges on these distinctions.
AN EMPIRICAL METHODOLOGY FOR DETERMINING THE MAGNITUDE OF SECTORAL INTERRELATIONSHIPS WITHIN THE INPUT-OUTPUT MATRIX FROM PRODUCTION AND USES TABLES FOR THE CASE OF THE UNITED STATES 1997-2019
This article outlines an empirical approach to analyzing the interconnectedness of production in the U.S. economy from 1997 to 2019. Utilizing make and use tables, it constructs time series to measure output-input associations across 71 industries. The research emphasizes linkages and the importance of economic data validation and replication.