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| Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel× | Panel Vektor Autoregresszió (Panel VAR)× | Kvantilis regresszió× | |
|---|---|---|---|
| Tudományterület | Ökonometria | Ökonometria | Ökonometria |
| Módszercsalád | Regression model | Regression model | Regression model |
| Keletkezés éve≠ | 2019 | 1988 | 1978 |
| Megalkotó≠ | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen | Koenker & Bassett |
| Típus≠ | Linear regression | Panel vector autoregression | Conditional quantile regression |
| Alapmű≠ | 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 ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alternatív nevek≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | PVAR, panel vector autoregression, Panel VAR (PVAR) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Kapcsolódó≠ | 5 | 3 | 5 |
| Összefoglaló≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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