Methoden vergelijken

	Markov Regime-Switching Model (MS-AR / MS-VAR)	ARIMA (Autoregressive Integrated Moving Average) Model	GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	1989	2015	1986
Grondlegger	Hamilton (1989); Kim & Nelson (1999)	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev
Type	Regime-switching time series model	Univariate time-series model	Conditional volatility model
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.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Aliassen	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli
Verwant	5	5	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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.