Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| DCC-MIDAS× | Proyecciones Locales× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2013 | 2005 |
| Autor original≠ | Engle, Ghysels, and Sohn | Oscar Jorda |
| Tipo≠ | Time-varying correlation model | Multi-horizon regression |
| Fuente seminal≠ | Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797. DOI ↗ | Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182. DOI ↗ |
| Alias≠ | DCC mixed-frequency model | LP-IR, Multi-horizon regression |
| Relacionados | 3 | 3 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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