Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| NARDL Pecahan Struktur× | Model ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Bidang | Ekonometrik | Ekonometrik |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2014–2018 | 1970 |
| Pengasas≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); structural break extensions by subsequent applied researchers | George Box and Gwilym Jenkins |
| Jenis≠ | Nonlinear cointegration with structural breaks | Time series forecasting model |
| Sumber perintis≠ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | SB-NARDL, NARDL with structural breaks, nonlinear ARDL with break, asymmetric ARDL structural break | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Berkaitan | 6 | 6 |
| Ringkasan≠ | Structural Break NARDL extends the Nonlinear Autoregressive Distributed Lag (NARDL) bounds-testing framework by explicitly accommodating one or more structural breaks in the long-run relationship. It separates positive and negative changes in the regressor, tests for cointegration, and allows regime shifts, providing a richer picture of asymmetric and break-sensitive dynamics between variables. | 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. |
| ScholarGateSet data ↗ |
|
|