Sammenlign metoder

	Markov regime-switching model (MS-AR / MS-VAR)	ARIMA (Autoregressive Integrated Moving Average) Model	Generaliseret Autoregressiv Betinget Heteroskedasticitet (GARCH)
Fagområde	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Oprindelsesår	1989	2015	1986
Ophavsperson	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
Oprindelig kilde	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.
Aliasser	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
Relaterede	5	5	5
Resumé	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.