Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel ARIMA modelis× | ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1970s–2000s | 1970 |
| Autors≠ | Extension of Box-Jenkins ARIMA (Box & Jenkins, 1970) to panel settings; formalised in panel econometrics literature (Hsiao, 2003) | George Box and Gwilym Jenkins |
| Tips≠ | Time-series model applied to panel data | Time series forecasting model |
| Pirmavots≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Citi nosaukumi | Panel ARIMA, ARIMA for panel data, cross-sectional ARIMA, multi-unit ARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | The Panel ARIMA model extends the classical Box-Jenkins ARIMA framework to panel data, fitting autoregressive integrated moving-average dynamics to multiple cross-sectional units observed over time. It accommodates unit-specific short-run dynamics and non-stationarity, making it suitable for forecasting and dynamic analysis when both cross-sectional and temporal dimensions are present. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateDatu kopa ↗ |
|
|