Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kónya Būta Foršas Paneļa Grāndžera Kauzalitāte× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime≠ | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 2006 | 2014 |
| Autors≠ | László Kónya | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Non-parametric bootstrap hypothesis test | Panel data regression |
| Pirmavots≠ | Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi | Bootstrap Panel Causality Test, Kónya Panel Granger Causality, SUR-Based Bootstrap Causality, Kónya Önyükleme Nedensellik Testi | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | Introduced by László Kónya in 2006, this method tests Granger causality in heterogeneous panels by estimating a Seemingly Unrelated Regressions (SUR) system and deriving country-specific critical values through bootstrapping. Unlike pooled panel tests, it delivers a separate causality verdict for each cross-section, making it particularly valuable in applied macroeconomics and international economics when panel units are expected to behave differently. | 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). |
| ScholarGateDatu kopa ↗ |
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