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Fjūrjēra Grangera cēloniskuma tests×Robusta ARDL robežu pārbaude (Fourier)×
NozareEkonometrijaEkonometrija
SaimeRegression modelRegression model
Izcelsmes gads20162001-2021
AutorsEnders and JonesPesaran, Shin & Smith (ARDL foundation); Fourier extension by Nazlioglu and related authors
TipsCausality testCointegration / bounds test
PirmavotsEnders, 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 ↗Nazlioglu, S., Gormus, A., & Soytas, U. (2021). Oil prices and monetary policy in emerging markets: structural breaks, asymmetries, and Fourier approximations. Energy Economics, 95, 105119. link ↗
Citi nosaukumiFourier Granger causality test, Enders-Jones Granger causality, smooth structural break Granger test, spectral Granger causalityFourier ARDL, Fourier bounds testing, ARDL with Fourier approximation, F-ARDL cointegration test
Saistītās65
KopsavilkumsThe 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.The Fourier ARDL bounds test augments the Pesaran-Shin-Smith cointegration framework with trigonometric (Fourier) terms that capture gradual, smooth structural breaks in the data-generating process. It tests for a long-run level relationship between variables without requiring the researcher to specify the number, timing, or form of structural breaks in advance.
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ScholarGateSalīdzināt metodes: Fourier Granger Causality · Fourier ARDL Bounds Test. Izgūts 2026-06-19 no https://scholargate.app/lv/compare