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| Testy panelovej kointegrácie (Pedroni, Kao, Westerlund)× | Odhadovač rozšírenej priemernej skupiny (Augmented Mean Group, AMG)× | Regresia metódou najmenších štvorcov (OLS)× | |
|---|---|---|---|
| Odbor | Ekonometria | Ekonometria | Ekonometria |
| Rodina | Regression model | Regression model | Regression model |
| Rok vzniku≠ | 2004 | 2010 | 2019 |
| Tvorca≠ | Pedroni; Kao; Westerlund | Eberhardt & Teal; Bond & Eberhardt | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Panel cointegration test | Heterogeneous panel data estimator | Linear regression |
| Pôvodný zdroj≠ | Pedroni, P. (2004). Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econometric Theory, 20(3), 597–625. DOI ↗ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Ďalšie názvy≠ | Pedroni cointegration test, Kao cointegration test, Westerlund cointegration test, panel long-run equilibrium tests | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Príbuzné≠ | 3 | 4 | 5 |
| Zhrnutie≠ | Panel cointegration tests check whether a set of integrated variables share a stable long-run equilibrium relationship across a panel of cross-sectional units. Pedroni (1999, 2004) provides heterogeneous-panel tests with seven statistics, Kao (1999) gives an ADF-based homogeneous-panel test, and Westerlund (2007) adds error-correction-based tests robust to structural breaks and cross-sectional dependence. | The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results. | 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). |
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