Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kipimo cha Utafiti wa Granger cha Fourier× | Uchanganuzi wa Ugaidi wa Granger wa Vunja Muundo× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2016 | 1995-2010 |
| Mwanzilishi≠ | Enders and Jones | Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010) |
| Aina≠ | Causality test | Hypothesis test / time-series model |
| Chanzo asilia≠ | Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419. DOI ↗ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ |
| Majina mbadala | Fourier Granger causality test, Enders-Jones Granger causality, smooth structural break Granger test, spectral Granger causality | break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test |
| Zinazohusiana≠ | 6 | 3 |
| Muhtasari≠ | The Fourier Granger causality test extends the classic Granger causality framework by embedding low-frequency Fourier terms in the VAR equation, allowing the causal relationship to shift gradually over time without requiring the researcher to pre-specify the number or location of structural breaks. | Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables switches on, switches off, or changes direction over time. |
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