เปรียบเทียบวิธี

	แบบจำลองออโตริเกรสซีฟไม่เชิงเส้น (Nonlinear Autoregressive - NAR)	แบบจำลอง ARMA (Autoregressive Moving Average)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	1978-1990	1970
ผู้ริเริ่ม	Tong, H. (threshold AR); Terasvirta, T. (STAR variant)	George E. P. Box and Gwilym M. Jenkins
ประเภท	Nonlinear time series model	Time series model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	NAR model, nonlinear autoregression, NLAR, threshold autoregressive model	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
ที่เกี่ยวข้อง	6	5
สรุป	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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.