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| Robuste Zeitreihenanalyse× | Methode der kleinsten Quadrate (OLS)× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr | 2019 | 2019 |
| Urheber≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Robust time series model (AR / MA / ARIMA) | Linear regression |
| Wegweisende Quelle≠ | Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliasnamen | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019). | 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). |
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