Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo ARIMA (Autoregressive Integrated Moving Average)× | Regressão Quantílica× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2015 | 1978 |
| Autor original≠ | Box & Jenkins (Box-Jenkins methodology) | Koenker & Bassett |
| Tipo≠ | Univariate time-series model | Conditional quantile regression |
| 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 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Outros nomes | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relacionados | 5 | 5 |
| 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). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateConjunto de dados ↗ |
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