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	Fourier SVAR Model	Model Autoregresji Wektorowej z Fouriera	Model Autoregresji Wektorowej (VAR)
Dziedzina	Ekonometria	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model	Regression model
Rok powstania	2010s	2010s	2005
Twórca	Extension of Sims (1980) SVAR framework with Fourier-series smoothing, developed across multiple authors in 2010s	Enders & Lee; extended by Nazlioglu and others to VAR systems	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Typ	Structural time-series model	Multivariate time-series model	Multivariate time-series model
Źródło pierwotne	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.	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Inne nazwy	Fourier SVAR, Fourier structural VAR, Fourier-approximation SVAR, frequency-domain SVAR	Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Pokrewne	3	6	4
Podsumowanie	The Fourier SVAR model integrates Fourier series approximations into the structural VAR framework, allowing the model to capture smooth, gradual structural breaks and time-varying dynamics in multivariate time series without requiring a priori knowledge of break dates. It recovers structural shocks and their propagation effects while remaining robust to low-frequency parameter drift.	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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).