Porovnať metódy

	Fourierov štrukturálny vektorový autoregresný (Fourier SVAR) model	Bayesovský VAR model (BVAR)
Odbor	Ekonometria	Ekonometria
Rodina	Regression model	Regression model
Rok vzniku	2010s	1984
Tvorca	Extension of Sims (1980) SVAR framework with Fourier-series smoothing, developed across multiple authors in 2010s	Doan, Litterman & Sims
Typ	Structural time-series model	Multivariate time-series model
Pôvodný zdroj	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.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Ďalšie názvy	Fourier SVAR, Fourier structural VAR, Fourier-approximation SVAR, frequency-domain SVAR	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Príbuzné	3	5
Zhrnutie	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 Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.