Comparer des méthodes

	Régression MIDAS : Prévision sur des fréquences de données mixtes	Modèle ARIMA (Autoregressive Integrated Moving Average)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2007	2015
Auteur d'origine	Eric Ghysels, Arthur Sinko & Rossen Valkanov	Box & Jenkins (Box-Jenkins methodology)
Type	Parametric mixed-frequency forecasting model	Univariate time-series model
Source fondatrice	Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90.	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
Alias	Mixed Frequency Regression, Mixed Data Sampling Model, High-Frequency Forecasting Regression, MIDAS Regresyonu	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Apparentées	3	5
Résumé	MIDAS (Mixed Data Sampling) Regression is an econometric framework that directly incorporates high-frequency predictors into models for lower-frequency outcome variables without requiring temporal aggregation of the regressors. Introduced by Eric Ghysels, Arthur Sinko, and Rossen Valkanov in 2007, MIDAS uses parsimoniously parameterized lag polynomials — such as the Beta or Exponential Almon weighting schemes — to summarize the information content of many high-frequency lags while avoiding parameter proliferation.	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).