Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Filtro de Kalman× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Área≠ | Finanças | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1989 | 2015 |
| Autor original≠ | Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching) | Box & Jenkins (Box-Jenkins methodology) |
| Tipo≠ | Linear state-space model | Univariate time-series model |
| Fonte seminal≠ | Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Outros nomes≠ | state-space model, dynamic linear model, recursive Bayesian filter, Kalman Filtresi — Finansal Durum Uzayı Modeli | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Relacionados | 5 | 5 |
| Resumo≠ | The Kalman filter is a recursive algorithm that estimates financial models with time-varying parameters, hidden factors, and noisy observations inside a dynamic state-space framework. The structural time series treatment was set out by Harvey (1989), with state-space and regime-switching extensions developed by Kim and Nelson (1999); it is widely applied to pairs trading, time-varying beta estimation, and yield-curve modelling. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
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