Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Parastā mazāko kvadrātu (OLS) regresija× | SARIMA (Seasonālais ARIMA)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2019 | 2015 |
| Autors≠ | Wooldridge (textbook treatment); classical least squares | Box & Jenkins (seasonal extension of ARIMA) |
| Tips≠ | Linear regression | Seasonal time-series model |
| Pirmavots≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Citi nosaukumi≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. |
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