Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| ARIMA mudel (autoregressiivne integreeritud libisev keskmine)× | Autoregressiivne mudel (AR)× | Mitte lineaarne ARDL (NARDL) mudel× | |
|---|---|---|---|
| Valdkond | Ökonomeetria | Ökonomeetria | Ökonomeetria |
| Perekond | Regression model | Regression model | Regression model |
| Tekkeaasta≠ | 1970 | 1970s (popularised 1976) | 2014 |
| Looja≠ | George Box and Gwilym Jenkins | George E. P. Box and Gwilym M. Jenkins | Shin, Yu & Greenwood-Nimmo |
| Tüüp≠ | Time series forecasting model | Time series model | Nonlinear cointegration model |
| Algallikas≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | 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 ↗ |
| Rööpnimetused | 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 |
| Seotud≠ | 6 | 6 | 5 |
| Kokkuvõte≠ | 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. |
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