Work

Factor Analysis for Multivariate Compositional Data and an Economic Application

Year: 2024

Type: article

Abstract: This paper aims to address the lack of appropriate methods for analysing multivariate compositional data by introducing FAMCoDa, a factor analysis model tailored for such data, along with a two-step estimation method.First, we outline the mathematical framework of FAMCoDa and the procedures for estimating factor loadings and scores, validating its effectiveness through simulation experiments.We then apply FAMCoDa to analyse the industrial consumption structure data of 41 countries.The results reveal that FAMCoDa efficiently manages multivariate compositional data, identifying correlations between variables.Our contributions are threefold: (1) presenting a novel factor analysis model for multivariate compositional data, focusing on inter-variable correlations, unlike existing models; (2) devising a two-step estimation process, starting with multivariate CoDa PCA for initial loadings and refining them through variance rotation, followed by ilr transformation and OLS regression for factor scores; (3) ensuring the compositional structure of the extracted factors remains consistent with the original variables.This work has significant implications for economic applications.

Source: ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH

DOI: 10.24818/18423264/58.4.24.05

Authors: GUAN Rong, WANG Huijuan, GUO Lijuan

Institutions: Central University of Finance and Economics, Beijing Technology and Business University

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Central University of Finance and Economics, Ministry of Education, India, National Social Science Fund of China

Award: 大规模网络结构数据的统计建模：理论与应用研究