Uporedite metode

	Nelinearni ARIMA model	ARIMA model (Autoregresivni integrisani model pokretnih proseka)
Oblast	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model
Godina nastanka	1978-1994	1970
Tvorac	Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions)	George Box and Gwilym Jenkins
Tip	Nonlinear time series model	Time series forecasting model
Temeljni izvor	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522249	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Drugi nazivi	nonlinear ARIMA, NARIMA, nonlinear time series model, nonlinear Box-Jenkins model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Srodne	3	6
Sažetak	The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.