Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo MA con Parámetros Variables en el Tiempo× | Modelo de Media Móvil (MA)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1990s | 1970 |
| Autor original≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. | Box and Jenkins |
| Tipo≠ | Time-varying state-space model | Linear time series model |
| Fuente seminal≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969 | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Alias | TVP-MA model, state-space MA, Kalman filter MA, time-varying MA | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The time-varying parameter moving average (TVP-MA) model extends the standard MA model by allowing the moving-average coefficients to change over time. Cast as a state-space system, it is estimated via the Kalman filter and smoother, making it well suited for series where the shock-transmission dynamics evolve across the sample. | The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods. |
| ScholarGateConjunto de datos ↗ |
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