Salīdzināt metodes

	Kopneses un gludās pārejas VAR (TVAR / STVAR)	ARCH-LM tests par atklātām heteroskedastiskuma kļūdām	GJR-GARCH (Asimetriskais GARCH)
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1998	1982	1993
Autors	Tsay (multivariate threshold modelling)	Robert F. Engle	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)
Tips	Nonlinear multivariate time-series model	Lagrange multiplier diagnostic test for conditional heteroscedasticity	Asymmetric conditional volatility model
Pirmavots	Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.	Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.	Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.
Citi nosaukumi	TVAR, STVAR, regime-switching VAR, threshold VAR	ARCH-LM Testi ve Volatilite Kümelenmesi Analizi, ARCH LM test, Engle's ARCH test, test for autoregressive conditional heteroscedasticity	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)
Saistītās	5	6	5
Kopsavilkums	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.	The ARCH-LM test is Robert Engle's (1982) Lagrange multiplier diagnostic for autoregressive conditional heteroscedasticity in the residuals of a fitted time-series model. It checks whether the error variance changes over time and clusters into calm and turbulent periods, and it is the standard pre-test run before fitting a GARCH-family volatility model.	GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).