Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo ARIMA (Autoregressive Integrated Moving Average)× | Modelo de Vetores Autorregressivos (VAR)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1970 | 2005 |
| Autor original≠ | George Box and Gwilym Jenkins | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Tipo≠ | Time series forecasting model | Multivariate time-series model |
| Fonte seminal≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Outros nomes | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
| ScholarGateConjunto de dados ↗ |
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