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| DCC-MIDAS× | GARCH-MIDAS× | Lokale projektioner× | |
|---|---|---|---|
| Fagområde | Økonometri | Økonometri | Økonometri |
| Familie | Regression model | Regression model | Regression model |
| Oprindelsesår≠ | 2013 | 2012 | 2005 |
| Ophavsperson≠ | Engle, Ghysels, and Sohn | Engle and Ghysels | Oscar Jorda |
| Type≠ | Time-varying correlation model | Time-varying variance model | Multi-horizon regression |
| Oprindelig kilde≠ | Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797. DOI ↗ | Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391. link ↗ | Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182. DOI ↗ |
| Aliasser≠ | DCC mixed-frequency model | Mixed-frequency volatility model | LP-IR, Multi-horizon regression |
| Relaterede | 3 | 3 | 3 |
| Resumé≠ | 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. | Local Projections (LP) is a semi-parametric method for estimating impulse responses directly via multi-horizon regressions, bypassing VAR-model specification. Introduced by Jorda (2005), it projects outcomes h periods ahead onto current shocks and lags, producing impulse-response functions without assuming a particular lag structure or VAR order. This flexibility has made it the dominant approach in applied macroeconomics for measuring policy effects and shock transmission. |
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