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Statistical Properties in Firms' Large-scale Data

ISBN-13: 9789811622991 / Angielski / Miękka / 2022

Atushi Ishikawa
Statistical Properties in Firms' Large-scale Data Ishikawa, Atushi 9789811622991 Springer Nature Singapore - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Statistical Properties in Firms' Large-scale Data

ISBN-13: 9789811622991 / Angielski / Miękka / 2022

Atushi Ishikawa
cena 443,82
(netto: 422,69 VAT:  5%)

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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.

Kategorie:
Nauka, Ekonomia i biznes
Kategorie BISAC:
Business & Economics > Makroekonomia
Business & Economics > Economics - Theory
Business & Economics > Statystyka gospodarcza
Wydawca:
Springer Nature Singapore
Seria wydawnicza:
Evolutionary Economics and Social Complexity Science
Język:
Angielski
ISBN-13:
9789811622991
Rok wydania:
2022
Waga:
0.25 kg
Wymiary:
23.5 x 15.5
Oprawa:
Miękka
Dodatkowe informacje:
Wydanie ilustrowane

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?.

Atushi Ishikawa, Kanazawa Gakuin University

The author was originally a theoretical physicist of elementary particles. He now specializes in Econophysics and is primarily engaged in the study of the statistical properties of firms’ large-scale financial data. The study covers a wide range of other topics, including analyzing point-of-sale (POS) data, analyzing Twitter, and analyzing land prices.


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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