手法を比較

	フーリエ構造ベクトル自己回帰 (Fourier SVAR) モデル	ベイズ型VARモデル（BVAR）	ベクトル自己回帰（VAR）モデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2010s	1984	2005
提唱者	Extension of Sims (1980) SVAR framework with Fourier-series smoothing, developed across multiple authors in 2010s	Doan, Litterman & Sims	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
種類	Structural time-series model	Multivariate time-series model	Multivariate time-series model
原典	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
別名	Fourier SVAR, Fourier structural VAR, Fourier-approximation SVAR, frequency-domain SVAR	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
関連	3	5	4
概要	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.	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).