Comparer des méthodes

	Modèle Markovien à Changement de Régime pour Séries Financières	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Finance	Économétrie
Famille	Regression model	Regression model
Année d'origine	1989	2019
Auteur d'origine	James D. Hamilton	Wooldridge (textbook treatment); classical least squares
Type	Markov regime-switching time-series 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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	1	5
Résumé	The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time.	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).