Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| DCC-MIDAS× | U-MIDAS (Unrestricted MIDAS)× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2013 | 2007 |
| Grondlegger≠ | Engle, Ghysels, and Sohn | Eric Ghysels |
| Type≠ | Time-varying correlation model | Time-series regression |
| Oorspronkelijke bron≠ | Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797. DOI ↗ | Foroni, C., Ghysels, E., & Marcellino, M. (2015). Mixed-frequency vector autoregressive models. International Journal of Forecasting, 31(4), 1051-1070. DOI ↗ |
| Aliassen | DCC mixed-frequency model | Unrestricted Mixed Data Sampling |
| Verwant | 3 | 3 |
| Samenvatting≠ | 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. | 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. |
| ScholarGateGegevensset ↗ |
|
|