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	Nonlinear ARIMA Model	वेक्टर ऑटोरिग्रेशन (VAR) मॉडल
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1978-1994	2005
प्रवर्तक	Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
प्रकार	Nonlinear time series model	Multivariate time-series model
मौलिक स्रोत	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522249	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
उपनाम	nonlinear ARIMA, NARIMA, nonlinear time series model, nonlinear Box-Jenkins model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
संबंधित	3	4
सारांश	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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).