Salīdzināt metodes

	Nelineārais ARMA modelis (NARMA)	ARMA modelis (Autoregresīvs vidējais aritmētiskais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1980s–1990s	1970
Autors	Tong (1990); Granger & Terasvirta (1993)	George E. P. Box and Gwilym M. Jenkins
Tips	Nonlinear time series model	Time series model
Pirmavots	Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198522300	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Citi nosaukumi	NARMA, nonlinear ARMA, NLARMA, nonlinear autoregressive moving average	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
Saistītās	2	5
Kopsavilkums	The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial 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.