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	Tröskel- och glidande övergångs-VAR (TVAR / STVAR)	ARCH-LM-testen för volatilitetskluster	Markovregimskiftesmodell (MS-AR / MS-VAR)
Ämnesområde	Ekonometri	Ekonometri	Ekonometri
Familj	Regression model	Regression model	Regression model
Ursprungsår	1998	1982	1989
Upphovsperson	Tsay (multivariate threshold modelling)	Robert F. Engle	Hamilton (1989); Kim & Nelson (1999)
Typ	Nonlinear multivariate time-series model	Lagrange multiplier diagnostic test for conditional heteroscedasticity	Regime-switching time series model
Ursprungskälla	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.	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	ARCH-LM Testi ve Volatilite Kümelenmesi Analizi, ARCH LM test, Engle's ARCH test, test for autoregressive conditional heteroscedasticity	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
Närliggande	5	6	5
Sammanfattning	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.	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.