Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo AR de Fourier× | Modelo ARMA (Média Móvel Autorregressiva)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2012 | 1970 |
| Autor original≠ | Enders & Lee | George E. P. Box and Gwilym M. Jenkins |
| Tipo≠ | Time series model with Fourier augmentation | Time series model |
| Fonte seminal≠ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Outros nomes | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | The Fourier AR model extends the standard autoregressive specification by adding trigonometric (sine and cosine) terms to the deterministic component. This allows the model to capture smooth, gradual shifts in the mean or trend of a time series without requiring the researcher to locate or count structural break points explicitly. | 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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