Methoden vergelijken

	Markov Regime-Switching Model (MS-AR / MS-VAR)	Vector Autoregressie met Drempelwaarde en Gladde Overgang (TVAR / STVAR)	Vector Autoregressie (VAR)-model
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	1989	1998	2005
Grondlegger	Hamilton (1989); Kim & Nelson (1999)	Tsay (multivariate threshold modelling)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	Regime-switching time series model	Nonlinear multivariate time-series model	Multivariate time-series model
Oorspronkelijke bron	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Aliassen	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	TVAR, STVAR, regime-switching VAR, threshold VAR	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Verwant	5	5	4
Samenvatting	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.	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).