Сравнение методов

	Нелинейная авторегрессионная (NAR) модель	Модель нелинейной авторегрессии с распределенным лагом (NARDL)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1978-1990	2014
Автор метода	Tong, H. (threshold AR); Terasvirta, T. (STAR variant)	Shin, Yu & Greenwood-Nimmo
Тип	Nonlinear time series model	Nonlinear cointegration model
Основополагающий источник	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.
Другие названия	NAR model, nonlinear autoregression, NLAR, threshold autoregressive model	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Связанные	6	5
Сводка	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 Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.