Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul ARMA cu Parametri Variabili în Timp (TVP-ARMA)× | Modelul ARMA (Autoregresiv Medie Mobilă)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1976 | 1970 |
| Autorul original≠ | Cooley & Prescott (1976); further formalised by Harvey (1989) | George E. P. Box and Gwilym M. Jenkins |
| Tip≠ | State-space time series model | Time series model |
| Sursa seminală≠ | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Denumiri alternative | TVP-ARMA, time-varying ARMA, state-space ARMA, locally stationary ARMA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
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