เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Panel Vector Autoregression (Panel VAR)× | การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)× | แบบจำลอง Vector Autoregression (VAR)× | |
|---|---|---|---|
| สาขาวิชา | เศรษฐมิติ | เศรษฐมิติ | เศรษฐมิติ |
| ตระกูล | Regression model | Regression model | Regression model |
| ปีกำเนิด≠ | 1988 | 2019 | 2005 |
| ผู้ริเริ่ม≠ | Holtz-Eakin, Newey & Rosen | Wooldridge (textbook treatment); classical least squares | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| ประเภท≠ | Panel vector autoregression | Linear regression | Multivariate time-series model |
| แหล่งต้นตำรับ≠ | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| ชื่อเรียกอื่น≠ | PVAR, panel vector autoregression, Panel VAR (PVAR) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| ที่เกี่ยวข้อง≠ | 3 | 5 | 4 |
| สรุป≠ | 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). | 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). |
| ScholarGateชุดข้อมูล ↗ |
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