Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Paneelvektori autoregressioon (Paneel VAR)× | Paneelide andmete fikseeritud efektide mudel× | Vektorautoregressiooni (VAR) mudel× | |
|---|---|---|---|
| Valdkond | Ökonomeetria | Ökonomeetria | Ökonomeetria |
| Perekond | Regression model | Regression model | Regression model |
| Tekkeaasta≠ | 1988 | 2014 | 2005 |
| Looja≠ | Holtz-Eakin, Newey & Rosen | Hsiao (textbook treatment); within transformation of panel data | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Tüüp≠ | Panel vector autoregression | Panel data regression | Multivariate time-series model |
| Algallikas≠ | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Rööpnimetused≠ | PVAR, panel vector autoregression, Panel VAR (PVAR) | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Seotud≠ | 3 | 5 | 4 |
| Kokkuvõte≠ | 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. | 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). |
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