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| Dumitrescu-Hurlin面板格兰杰因果检验× | 格兰杰因果检验× | 面板数据固定效应模型× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族≠ | Hypothesis test | Regression model | Regression model |
| 起源年份≠ | 2012 | 1969 | 2014 |
| 提出者≠ | Elena-Ivona Dumitrescu & Christophe Hurlin | Clive W. J. Granger | Hsiao (textbook treatment); within transformation of panel data |
| 类型≠ | Non-causality test for heterogeneous panels | Time-series predictive causality test | Panel data regression |
| 开创性文献≠ | Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| 别名 | DH Causality Test, Panel Granger Causality Test (Heterogeneous), Dumitrescu-Hurlin Test, Heterojen Panel Nedensellik Testi | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| 相关≠ | 3 | 5 | 5 |
| 摘要≠ | The Dumitrescu-Hurlin (DH) test, introduced by Elena-Ivona Dumitrescu and Christophe Hurlin in their 2012 Economic Modelling article, tests for Granger non-causality in heterogeneous panel datasets. Unlike standard panel causality approaches, it permits each cross-sectional unit to have its own distinct causal relationship, making it well-suited for macro-panels of countries, firms, or regions where homogeneity cannot be assumed. | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. | 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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