經濟學에 있어서의 要因分析의 適用

• 영문명: The Application of Factor Analysis in Economics
• 발행기관: 서울대학교 경제연구소
• 저자명: Hyung-Yoon Byun(邊衡尹)
• 간행물 정보: 『경제논집』경제논집 5권 2호, 1~23쪽, 전체 22쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 1966.06.30

영문 초록

I. Preface Factor analysis method, which is an applied branch of mathematics, was devised by J. C. Spearman in 1904, and grew up in this century with the field of psychology. It is increasingly being adopted by other fields, such as economics and sociology. In economics it is applied in economic growth or economic development theory. The present author is especially interested in the latter applications. But before examining the core of the problem, it may well examine factor analysis itself. And R. Stone's 1947 paper, which deals with the application of the factor analysis to statistical technique, and which is the basis of T. Kloek and L.B.M. Mennes' most advanced paper of the field, is useful for understanding the implication of factor analysis method. In this paper, therefore, I introduce factor analysis first, examine Stone's paper next, and then M. Megee's paper and I. Adelman and C. T. Morris' paper on the applications of factor analysis to economic development theory. II. Factor Analysis Factor analysis method is applied to economics in the form of principal component analysis which was devised by Hotelling to deal with the problem of factor analysis in psychology. By principal component or factor analysis we try to answer the question: Is it possible to analyze a set of variables into a more fundamental set of independent components (or factors) possibly fewer in number? Which portion of the total variance can be accounted for by each component? Here, Hotelling' s method of solution is introduced. Assume that we want to replace a set of standardized variables zi (i = 1, 2, …, p) by a more fundamental set of variables ui (i=1, 2, …, p), such that where z is a p-component column vector, K is a p X p matrix of coefficients, and u, a p-component column vector. The components ui are principal components and are orthogonal to each other. We want to maximize the contribution of the first principal component u₁: i.e., maximize S₁, under condition that where R is a p x p symmetric matrix of correlations between the variables zi; and K', the transpose of K. Here it is found that the solutions of ki₁ are the components of eigenvector of matrix R corresponding to the largest eigenvalue λ₁ of that matrix multiplied by the square root of λ₁. Similarly we can find second component (or factor), third component, and so on. III. On Application of Factor Analysis to Statistical Technique. R. Stone has applied the method of factor analysis or principal components to national income data of American economy taken from the years 1922-38. Here seventeen variables are reduced to three factors and these three factors together account for more than 97 per cent of the total variance of all variables. Moreover, the three largest factors can tentatively be identified with income (or output), rate of changes of income, and time trend respectively. T. Klok and L. B. M. Mennes have, in their 1960 paper, furthered Stone's analysis and give new economic meaning to that method. IV. On Application of Factor Analysis to Economic Development Theory, I. M. Megee's paper purposes to discuss the usefulness of mathematical model for analyzing problems of economic growth on the one hand, and to analyze briefly the findings from some particular implementations of factor analysis with the hope of discovering some advantages, disadvantages, and implications for the use of the method in economic growth analysis and planning for future growth on the other. Our interest is in the latter. She introduces three techniques: R, Q, and M-techniques. These there techniques provide the means of testing three kinds of hypotheses related to economic development: R can test hypotheses about variables causing economic development, using the factor loadings; Q can test hypotheses regarding the area differentiation of patterns of economic development, using the factor scores; and M can test hypotheses involving changes of variables or regions ove

목차

Ⅰ. 序言

Ⅱ. 要因分析

Ⅲ. 統計技術的 適用에 대하여

Ⅳ. 經濟發展論에의 適用에 대하여(其1)

Ⅴ. 經濟發展論에의 適用에 대하여(其2)

Ⅵ. 結言