Compara mètodes

	Model d'ARIMA (Autoregressive Integrated Moving Average)	Regressió quantílica
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2015	1978
Autor original	Box & Jenkins (Box-Jenkins methodology)	Koenker & Bassett
Tipus	Univariate time-series model	Conditional quantile regression
Font 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.
Àlies	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionats	5	5
Resum	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.