Comparer des méthodes

	Modèle à changement de régime markovien (MS-AR / MS-VAR)	Autoregressive Conditional Heteroskedasticity généralisée (GARCH)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1989	1986	2019
Auteur d'origine	Hamilton (1989); Kim & Nelson (1999)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Type	Regime-switching time series model	Conditional volatility model	Linear regression
Source fondatrice	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5	5
Résumé	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.	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).