Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo ARIMA (Autoregressive Integrated Moving Average)× | Modelo de Vetor de Correção de Erros (VECM)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2015 | 1987 |
| Autor original≠ | Box & Jenkins (Box-Jenkins methodology) | Engle & Granger |
| Tipo≠ | Univariate time-series model | Multivariate time-series model |
| Fonte seminal≠ | 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 | Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. DOI ↗ |
| Outros nomes≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli) |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | 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). | The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework. |
| ScholarGateConjunto de dados ↗ |
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