手法を比較
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| 構造変化固定効果モデル× | 構造的ブレーク・ランダム効果モデル× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1998 (Bai-Perron); FE estimator classical | 1998–2000s |
| 提唱者≠ | Bai & Perron (structural break testing); Mundlak / within-group estimator tradition | Bai & Perron (break detection); Baltagi (panel RE framework) |
| 種類≠ | Panel regression with regime change | Panel regression with regime shifts |
| 原典≠ | 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 ↗ |
| 別名 | 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 |
| 関連≠ | 6 | 5 |
| 概要≠ | 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. |
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