Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo de Efectos Fijos con Ruptura Estructural× | Modelo de Efectos Aleatorios con Rupturas Estructurales× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1998 (Bai-Perron); FE estimator classical | 1998–2000s |
| Autor original≠ | Bai & Perron (structural break testing); Mundlak / within-group estimator tradition | Bai & Perron (break detection); Baltagi (panel RE framework) |
| Tipo≠ | Panel regression with regime change | Panel regression with regime shifts |
| Fuente seminal≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78. DOI ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Alias | FE model with structural breaks, break-adjusted fixed effects, panel fixed effects with regime shifts, structural change fixed effects estimator | RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The structural break fixed effects model extends the standard within-group (FE) panel estimator by allowing the slope coefficients to shift at one or more detected break dates. Each unit's unobserved time-invariant heterogeneity is still removed by demeaning, but separate coefficient regimes are estimated for each sub-period, capturing policy shifts, crises, or technological transitions that would otherwise bias a single-regime FE estimate. | The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter estimates while retaining the efficiency gains of pooling individual-level variation as random draws from a common distribution. |
| ScholarGateConjunto de datos ↗ |
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