Σύγκριση μεθόδων

	Μοντέλο Μαρκοβιανής Εναλλαγής Καθεστώτων (MS-AR / MS-VAR)	Γενικευμένη Αυτοπαλίνδρομη Υπό Συνθήκη Ετεροσκεδαστικότητα (GARCH)	Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)
Πεδίο	Οικονομετρία	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	1989	1986	2019
Δημιουργός	Hamilton (1989); Kim & Nelson (1999)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Τύπος	Regime-switching time series model	Conditional volatility model	Linear regression
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	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
Συναφείς	5	5	5
Σύνοψη	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).