方法对比
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| 高频数据与市场微观结构分析× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 金融学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2007 | 2019 |
| 提出者≠ | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Market microstructure / high-frequency econometrics | Linear regression |
| 开创性文献≠ | 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 |
| 别名 | 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 |
| 相关 | 5 | 5 |
| 摘要≠ | 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). |
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