Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļa SARIMA modelis× | Panel Data Analysis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1976 (SARIMA); 1990s (panel extensions) | 1966–1978 |
| Autors≠ | Box & Jenkins (SARIMA foundation); panel extension via mean-group and pooled estimators | Balestra & Nerlove (1966); Mundlak (1978); Hausman (1978) |
| Tips≠ | Seasonal time series panel model | Panel regression framework |
| Pirmavots≠ | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control. Holden-Day. ISBN: 978-0470272848 | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030539528 |
| Citi nosaukumi | Panel SARIMA, Seasonal ARIMA panel model, SARIMA panel estimation, grouped seasonal time series model | longitudinal data analysis, pooled cross-sectional time-series analysis, panel regression, data panel analysis |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Panel SARIMA model applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) framework to panel data, fitting individual or pooled seasonal time series models across multiple cross-sectional units. It captures both non-seasonal and seasonal autocorrelation, trends, and periodicity, making it suitable for datasets where multiple entities share a common seasonal structure over time. | Panel data analysis models data that track multiple units — countries, firms, individuals — over time, enabling researchers to control for unobserved unit-level heterogeneity that would otherwise bias cross-sectional or time-series estimates. The two core specifications are fixed effects and random effects, selected via the Hausman test. |
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