Statistical properties in firms' large-scale data [electronic resource]

• 作者： Ishikawa, Atushi.
• 其他作者：
  • SpringerLink (Online service)
• 其他題名：
  • Evolutionary economics and social complexity science ;
• 出版： Singapore : Springer Singapore :Imprint: Springer
• 叢書名： Evolutionary economics and social complexity science,v.26
• 主題： Finance--Statistics. , Macroeconomics/Monetary Economics//Financial Economics. , Heterodox Economics. , Statistics for Business, Management, Economics, Finance, Insurance. , Statistics for Social Sciences, Humanities, Law.
• ISBN： 9789811622977 (electronic bk.) 、 9789811622960 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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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• 摘要註: 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.
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