Krahasoni metodat

	Modeli ARIMA (Autoregresiv i Integruar Mesatar Lëvizës)	Modeli Autoregresiv (AR)	Model ARDL jolinear (NARDL)
Fusha	Ekonometri	Ekonometri	Ekonometri
Familja	Regression model	Regression model	Regression model
Viti i origjinës	1970	1970s (popularised 1976)	2014
Krijuesi	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins	Shin, Yu & Greenwood-Nimmo
Lloji	Time series forecasting model	Time series model	Nonlinear cointegration model
Burimi themelues	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.
Emërtime të tjera	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	AR model, AR(p) model, autoregression, AR process	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Të lidhura	6	6	5
Përmbledhja	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.	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.