Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на високочестотни данни и пазарна микроструктура× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Финанси | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2007 | 2019 |
| Създател≠ | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Market microstructure / high-frequency econometrics | Linear regression |
| Основополагащ източник≠ | Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateНабор от данни ↗ |
|
|