Methoden vergelijken

	Markov Regime-Switching Model (MS-AR / MS-VAR)	Exponential GARCH (EGARCH)	Gewone Kleinste Kwadraten (GKK) Regressie
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	1989	1991	2019
Grondlegger	Hamilton (1989); Kim & Nelson (1999)	Nelson	Wooldridge (textbook treatment); classical least squares
Type	Regime-switching time series model	Conditional volatility model (asymmetric GARCH variant)	Linear regression
Oorspronkelijke bron	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Aliassen	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Verwant	5	4	5
Samenvatting	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.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	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).