Sammenlign metoder

	Terskler- og glidende overgangs-VAR (TVAR / STVAR)	Eksponentiell GARCH (EGARCH)	Markov-regimeskiftmodell (MS-AR / MS-VAR)
Fagfelt	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	1998	1991	1989
Opphavsperson	Tsay (multivariate threshold modelling)	Nelson	Hamilton (1989); Kim & Nelson (1999)
Type	Nonlinear multivariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Regime-switching time series model
Opprinnelig kilde	Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Alias	TVAR, STVAR, regime-switching VAR, threshold VAR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
Relaterte	5	4	5
Sammendrag	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.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	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.