Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo ARMA com Parâmetros Variáveis no Tempo (TVP-ARMA)× | Modelo ARMA (Média Móvel Autorregressiva)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1976 | 1970 |
| Autor original≠ | Cooley & Prescott (1976); further formalised by Harvey (1989) | George E. P. Box and Gwilym M. Jenkins |
| Tipo≠ | State-space time series model | Time series model |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | TVP-ARMA, time-varying ARMA, state-space ARMA, locally stationary ARMA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Relacionados≠ | 3 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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