Comparer des méthodes

	Modèle de Vecteur Autorégressif Structurel à Fourier (SVAR-Fourier)	Modèle VAR bayésien (BVAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2010s	1984
Auteur d'origine	Extension of Sims (1980) SVAR framework with Fourier-series smoothing, developed across multiple authors in 2010s	Doan, Litterman & Sims
Type	Structural time-series model	Multivariate time-series model
Source fondatrice	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.
Alias	Fourier SVAR, Fourier structural VAR, Fourier-approximation SVAR, frequency-domain SVAR	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Apparentées	3	5
Résumé	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.