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| Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)× | Test de ruptures structurelles multiples de Bai-Perron× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille≠ | Regression model | Hypothesis test |
| Année d'origine≠ | 1970 | 1998 |
| Auteur d'origine≠ | George Box and Gwilym Jenkins | Jushan Bai & Pierre Perron |
| Type≠ | Time series forecasting model | Sequential hypothesis test for multiple structural breaks |
| Source fondatrice≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Alias | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi |
| Apparentées≠ | 6 | 2 |
| Résumé≠ | 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. | The Bai-Perron test, introduced by Jushan Bai and Pierre Perron in their landmark 1998 Econometrica paper, is a least-squares-based procedure for detecting, estimating, and testing the number of structural breaks in a linear regression model estimated on time-series data. Unlike single-break tests, it simultaneously identifies multiple change-points in a sample, providing economists and empirical researchers with a rigorous, data-driven way to locate parameter instability across time. |
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