Statistical properties in firms' large-scale data
著者
書誌事項
Statistical properties in firms' large-scale data
（Evolutionary economics and social complexity science, v. 26）
Springer, c2021
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This is the first book to provide a systematic description of statistical properties of large-scale financial data. Specifically, the power-law and log-normal distributions observed at a given time and their changes using time-reversal symmetry, quasi-time-reversal symmetry, Gibrat's law, and the non-Gibrat's property observed in a short-term period are derived here. The statistical properties observed over a long-term period, such as power-law and exponential growth, are also derived. These subjects have not been thoroughly discussed in the field of economics in the past, and this book is a compilation of the author's series of studies by reconstructing the data analyses published in 15 academic journals with new data. This book provides readers with a theoretical and empirical understanding of how the statistical properties observed in firms' large-scale data are related along the time axis. It is possible to expand this discussion to understand theoretically and empirically how the statistical properties observed among differing large-scale financial data are related. This possibility provides readers with an approach to microfoundations, an important issue that has been studied in economics for many years.
目次
Chapter 1. Introduction.- Chapter 2. Non-Gibrat's Property in the Mid-scale Range.- Chapter 3. Quasi-statistically Varying Power-law and Log-normal Distributions.- Chapter 4. Extension of Non-Gibrat's Property.- Chapter 5. Long-term Firm Growth Derived from Non-Gibrat's Property and Gibrat's Law.- Chapter 6. Firm-age Distribution and the Inactive Rate of Firms.- Chapter 7. Statistical Properties in Inactive Rate of Firms.- Chapter 8. Power Laws with Different Exponents in Firm-Size Variables.- Chapter 9. Why does Production Function Take the Cobb-Douglas Form?.
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