Comparer des méthodes

	Régression MIDAS sans restriction	DCC-MIDAS	GARCH-MIDAS
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2007	2013	2012
Auteur d'origine	Eric Ghysels	Engle, Ghysels, and Sohn	Engle and Ghysels
Type	Time-series regression	Time-varying correlation model	Time-varying variance model
Source fondatrice	Foroni, C., Ghysels, E., & Marcellino, M. (2015). Mixed-frequency vector autoregressive models. International Journal of Forecasting, 31(4), 1051-1070.	Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.	Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391.
Alias	Unrestricted Mixed Data Sampling	DCC mixed-frequency model	Mixed-frequency volatility model
Apparentées	3	3	3
Résumé	U-MIDAS (Unrestricted MIDAS) is a regression framework designed to handle mixed-frequency data—when explanatory variables arrive at different sampling frequencies (e.g., monthly GDP mixed with daily stock returns). Introduced by Ghysels and colleagues (2007), it eliminates the restrictive lag-structure polynomial constraints of the original MIDAS approach, allowing fuller use of high-frequency information. This flexibility makes it ideal for nowcasting and real-time economic forecasting.	DCC-MIDAS combines dynamic conditional correlation (DCC) GARCH with mixed-frequency data sampling (MIDAS), enabling estimation of time-varying correlations between variables when observations arrive at different frequencies. Introduced by Engle et al. (2013), it models how correlations evolve with low-frequency macroeconomic conditions using high-frequency asset price information. This is crucial for portfolio risk management and understanding macro-finance linkages.	GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by Engle and Ghysels (2012), this framework elegantly separates volatility time scales. The approach is powerful for understanding how macro conditions (growth, inflation) drive risk premia and for improved volatility forecasting.