Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Autoregresīvs modelis (AR)× | Paplašinātais Dikija-Fullera (ADF) vienības saknes tests× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1970s (popularised 1976) | 1979–1984 |
| Autors≠ | George E. P. Box and Gwilym M. Jenkins | Said & Dickey (1984); building on Dickey & Fuller (1979) |
| Tips≠ | Time series model | Hypothesis test (unit root) |
| Pirmavots≠ | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 | Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. DOI ↗ |
| Citi nosaukumi | AR model, AR(p) model, autoregression, AR process | ADF test, ADF unit root test, Dickey-Fuller test (augmented), Said-Dickey test |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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 Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance. |
| ScholarGateDatu kopa ↗ |
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