手法を比較

	U-MIDAS	GARCH-MIDAS
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2007	2012
提唱者	Eric Ghysels	Engle and Ghysels
種類	Time-series regression	Time-varying variance model
原典	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. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391.
別名	Unrestricted Mixed Data Sampling	Mixed-frequency volatility model
関連	3	3
概要	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.	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.