Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Fjēra nejaušo efektu modelis× | Strukturālās izmaiņas ar nejaušiem efektiem modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2006-2012 | 1998–2000s |
| Autors≠ | Becker, Enders & Lee; Enders & Lee | Bai & Perron (break detection); Baltagi (panel RE framework) |
| Tips≠ | Panel regression with Fourier approximation | Panel regression with regime shifts |
| Pirmavots≠ | Becker, R., Enders, W., & Lee, J. (2006). A stationary test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409. DOI ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Citi nosaukumi | Fourier RE model, FFF random effects, flexible Fourier random effects, Fourier augmented random effects | RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Fourier Random Effects Model extends the standard random effects panel estimator by incorporating trigonometric (Fourier) terms to approximate smooth, gradual structural change in time trends or intercepts. It retains the GLS efficiency advantages of the random effects estimator while allowing parameters to shift continuously over time without requiring knowledge of exact break dates. | 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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