Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Fourier Random Effects -malli× | Paneelin satunnaisvaikutusmalli× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2006-2012 | 1966 |
| Kehittäjä≠ | Becker, Enders & Lee; Enders & Lee | Balestra & Nerlove |
| Tyyppi≠ | Panel regression with Fourier approximation | Panel data estimator |
| Alkuperäislähde≠ | 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 ↗ | 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 ↗ |
| Rinnakkaisnimet | Fourier RE model, FFF random effects, flexible Fourier random effects, Fourier augmented random effects | random effects estimator, RE model, GLS random effects, error components model |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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 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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