Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beijesa Grangera cēloņsakarība× | Grindera koeficientālās pārbaudes panelim× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1969 (frequentist); 1984 (Bayesian treatment) | 1988–2012 |
| Autors≠ | Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature | Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012) |
| Tips≠ | Bayesian causal inference test | Causality test |
| Pirmavots≠ | Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier. link ↗ | Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. DOI ↗ |
| Citi nosaukumi | Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean | panel causality test, Dumitrescu-Hurlin test, heterogeneous panel causality, panel Granger test |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Bayesian Granger causality tests whether past values of one time series carry predictive information about another, framing the hypothesis through Bayesian inference rather than frequentist p-values. It combines a vector autoregressive (VAR) structure with prior distributions over coefficients and evaluates causal claims via posterior probabilities or Bayes factors, providing a probabilistic and nuanced alternative to the classical Granger test. | The Panel Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by pooling information across units. |
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