Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Data za Paneli za Fourier× | Mfumo wa Athari Nasibu za Paneli× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2006 (Fourier framework); panel extensions 2010s | 1966 |
| Mwanzilishi≠ | Becker, Enders, and Lee (Fourier unit root framework); extended to panel data by subsequent applied econometricians | Balestra & Nerlove |
| Aina≠ | Panel regression with Fourier terms | Panel data estimator |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | Fourier panel regression, smooth structural break panel model, trigonometric panel data model, Fourier-flexible panel estimator | random effects estimator, RE model, GLS random effects, error components model |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Fourier panel data analysis embeds trigonometric sine and cosine terms into a standard panel regression to approximate smooth, gradual structural shifts in the data-generating process. Rather than assuming a sharp break at a known date, the Fourier approach lets the data reveal the timing and shape of any structural change through a flexible trigonometric approximation, while retaining the cross-sectional and time-series structure of panel data. | 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. |
| ScholarGateSeti ya data ↗ |
|
|