Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa VAR wa Fourier× | Kipimo cha Utafiti wa Granger cha Fourier× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2010s | 2016 |
| Mwanzilishi≠ | Enders & Lee; extended by Nazlioglu and others to VAR systems | Enders and Jones |
| Aina≠ | Multivariate time-series model | Causality test |
| Chanzo asilia≠ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599. DOI ↗ | 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 ↗ |
| Majina mbadala | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR | Fourier Granger causality test, Enders-Jones Granger causality, smooth structural break Granger test, spectral Granger causality |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | The Fourier VAR model extends the standard Vector Autoregression by replacing fixed deterministic terms with Fourier trigonometric components, allowing the intercept (and optionally the trend) to shift gradually and smoothly over time. This eliminates the need to pre-specify the number, timing, or shape of structural breaks in a multivariate time-series system. | 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. |
| ScholarGateSeti ya data ↗ |
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