Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia MIDAS nerestricționată× | GARCH-MIDAS× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2007 | 2012 |
| Autorul original≠ | Eric Ghysels | Engle and Ghysels |
| Tip≠ | Time-series regression | Time-varying variance model |
| Sursa seminală≠ | Foroni, C., Ghysels, E., & Marcellino, M. (2015). Mixed-frequency vector autoregressive models. International Journal of Forecasting, 31(4), 1051-1070. DOI ↗ | Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391. link ↗ |
| Denumiri alternative | Unrestricted Mixed Data Sampling | Mixed-frequency volatility model |
| Înrudite | 3 | 3 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
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