השוואת שיטות

	אוטו-רגרסיה וקטורית בייסיאנית (BVAR)	מודל מיתוג-משטרים של מרקוב (MS-AR / MS-VAR)	Threshold and Smooth-Transition VAR
תחום	אקונומטריקה	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	1986	1989	1998
הוגה השיטה	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Hamilton (1989); Kim & Nelson (1999)	Tsay (multivariate threshold modelling)
סוג	Bayesian multivariate time-series model	Regime-switching time series model	Nonlinear multivariate time-series model
מקור מכונן	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.	Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.
כינויים	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	TVAR, STVAR, regime-switching VAR, threshold VAR
קשורות	5	5	5
תקציר	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.	Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences.