Masahiro Kuroda – författare

The outstanding economic performance of East Asian countries has been investigated in numerous studies. However, most comparative studies analyze macro-level productivity. In this book, the productivity performance of China, Korea, Japan, Taiwan and the United States are compared at industry level. The work is a result of an international collaborative research project by RIETI (Research Institute of Economy, Trade and Industry), Japan. The total factor productivity growth and level amongst these five countries sheds new light on the industrial competitiveness of growing Asian economies compared to Japan and the United States. In addition, this book provides detailed information on productivity datasets for these five countries.Productivity in Asia will strongly appeal to scholars of Asian studies, industrial organization and economics as well as those interested in productivity statistics.

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods. This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods.

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods. This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods.

This book explores the concept of “Science for REdesigning Science, Technology and Innovation Policy (SciREX)” as those policies are conducted in a scientific manner. It is the first attempt to measure the effects of science and technology policies and as well as industrial policies on total factor productivity (TFP). The evolution of science and technology in the twentieth century, known as Big Science, led to the realization of an economic structure of mass production and consumption through the expansion of tangible fixed assets. However, since the latter half of the twentieth century, the trend has shifted toward intangible intellectual assets, such as research and development and information processing, resulting in a significant structural change in the economy and society. The book scientifically investigates the effects of this intangible fixed asset investment on the economic structure, evaluates the direction of structural change, and determines the merits and demerits ofits policies based on the state of science and society in the trans-scientific era. To understand the impact of scientific knowledge and information on the economy and society, the book measures the volume of services of intellectual and information assets as intangible fixed assets and their spillover effects among industrial sectors. By measuring the TFP growth rate of industries, the economic spillover effects of intangible fixed asset outcomes are evaluated to measure the impact of science and technology and industrial policy as a scientific approach to science and technology policy.