Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panelu vektorautoregresijas (Panel VAR) modelis× | Parastā mazāko kvadrātu (OLS) regresija× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|---|
| Nozare | Ekonometrija | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model | Regression model |
| Izcelsmes gads≠ | 1988 | 2019 | 2014 |
| Autors≠ | Holtz-Eakin, Newey & Rosen | Wooldridge (textbook treatment); classical least squares | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Panel vector autoregression | Linear regression | Panel data regression |
| Pirmavots≠ | 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 | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi≠ | 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 |
| Saistītās≠ | 3 | 5 | 5 |
| Kopsavilkums≠ | 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). |
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