مقایسهٔ روش‌ها

	مدل خودرگرسیون برداری بیزی (BVAR)	مدل رژیم-سوئیچینگ مارکوف (MS-AR / MS-VAR)	مدل سری زمانی ساختاری (مدل ساختاری پایه)
حوزه	اقتصادسنجی	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1986	1989	1990
پدیدآور	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Hamilton (1989); Kim & Nelson (1999)	Andrew C. Harvey
نوع	Bayesian multivariate time-series model	Regime-switching time series model	State-space (unobserved components) time series model
منبع بنیادین	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
نام‌های دیگر	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
مرتبط	5	5	4
خلاصه	Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.	The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.	The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.