Сравнение методов

	Нелинейная авторегрессионная (NAR) модель	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)	Модель ARMA (авторегрессионная скользящая средняя)	Авторегрессионная модель (AR)	Модель нелинейной авторегрессии с распределенным лагом (NARDL)
Область	Эконометрика	Эконометрика	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model	Regression model	Regression model
Год появления	1978-1990	1970	1970	1970s (popularised 1976)	2014
Автор метода	Tong, H. (threshold AR); Terasvirta, T. (STAR variant)	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins	George E. P. Box and Gwilym M. Jenkins	Shin, Yu & Greenwood-Nimmo
Тип	Nonlinear time series model	Time series forecasting model	Time series model	Time series model	Nonlinear cointegration model
Основополагающий источник	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.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043	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	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	AR model, AR(p) model, autoregression, AR process	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Связанные	6	6	5	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 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.	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.	An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial 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.