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| Авторегресивен модел (AR)× | Нелинеен модел ARDL (NARDL)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1970s (popularised 1976) | 2014 |
| Създател≠ | George E. P. Box and Gwilym M. Jenkins | Shin, Yu & Greenwood-Nimmo |
| Тип≠ | Time series model | Nonlinear cointegration model |
| Основополагащ източник≠ | 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. link ↗ |
| Други названия | AR model, AR(p) model, autoregression, AR process | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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