Sammenlign metoder

	ARIMA (Autoregressive Integrated Moving Average) Model	Generaliseret Autoregressiv Betinget Heteroskedasticitet (GARCH)	Almindelig mindste kvadraters metode (OLS) regression	SARIMA (Seasonal ARIMA)
Fagområde	Økonometri	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model	Regression model
Oprindelsesår	2015	1986	2019	2015
Ophavsperson	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares	Box & Jenkins (seasonal extension of ARIMA)
Type	Univariate time-series model	Conditional volatility model	Linear regression	Seasonal time-series model
Oprindelig kilde	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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
Aliasser	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA
Relaterede	5	5	5	5
Resumé	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.	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).	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.