Methoden vergleichen
Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.
| Zeitvarianz-Parameter-Autoregression (TVP-AR)× | ARIMA-Modell (Autoregressives integriertes gleitendes Durchschnittsmodell)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1976–2005 | 1970 |
| Urheber≠ | Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005) | George Box and Gwilym Jenkins |
| Typ≠ | Time-series model with drifting coefficients | Time series forecasting model |
| Wegweisende Quelle≠ | Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Aliasnamen | TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Verwandt≠ | 4 | 6 |
| Zusammenfassung≠ | The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date. | 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. |
| ScholarGateDatensatz ↗ |
|
|