方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 随机效应面板模型× | 分层线性模型 (HLM / 多层模型)× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|---|
| 领域≠ | 计量经济学 | 统计学 | 计量经济学 |
| 方法族≠ | Regression model | Hypothesis test | Regression model |
| 起源年份≠ | 1978 | 1986 | 2019 |
| 提出者≠ | Baltagi (textbook treatment); Hausman specification test | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Panel data regression | Parametric nested-data regression | Linear regression |
| 开创性文献≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271. DOI ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名≠ | random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli | HLM, MLM, multilevel modeling, multilevel analysis | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 5 | 4 | 5 |
| 摘要≠ | The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data. | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. | 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). |
| ScholarGate数据集 ↗ |
|
|
|