مقایسهٔ روش‌ها

	مدل خودبازگشتی غیرخطی (NAR)	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)
حوزه	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	1978-1990	1970
پدیدآور	Tong, H. (threshold AR); Terasvirta, T. (STAR variant)	George Box and Gwilym Jenkins
نوع	Nonlinear time series model	Time series forecasting model
منبع بنیادین	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
نام‌های دیگر	NAR model, nonlinear autoregression, NLAR, threshold autoregressive model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
مرتبط	6	6
خلاصه	The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series.	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.