方法对比

	面板向量自回归模型 (Panel VAR)	普通最小二乘法 (OLS) 回归	面板数据固定效应模型	向量自回归 (VAR) 模型
领域	计量经济学	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	1988	2019	2014	2005
提出者	Holtz-Eakin, Newey & Rosen	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
类型	Panel vector autoregression	Linear regression	Panel data regression	Multivariate time-series model
开创性文献	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
别名	PVAR, panel vector autoregression, Panel VAR (PVAR)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
相关	3	5	5	4
摘要	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.	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).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).