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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Modello a Effetti Casuali con Rottura Strutturale× | Modello a Effetti Casuali Panel× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1998–2000s | 1966 |
| Ideatore≠ | Bai & Perron (break detection); Baltagi (panel RE framework) | Balestra & Nerlove |
| Tipo≠ | Panel regression with regime shifts | Panel data estimator |
| Fonte seminale≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ | Balestra, P., & Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34(3), 585–612. DOI ↗ |
| Alias | RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts | random effects estimator, RE model, GLS random effects, error components model |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation. |
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