方法对比
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| 普通最小二乘法 (OLS) 回归× | 面板向量自回归模型 (Panel VAR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2019 | 1988 |
| 提出者≠ | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen |
| 类型≠ | Linear regression | Panel vector autoregression |
| 开创性文献≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ |
| 别名≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | PVAR, panel vector autoregression, Panel VAR (PVAR) |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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). | Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level. |
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