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| ARIMA（自己回帰和分移動平均）モデル× | ダービン-ワトソン検定による自己相関の検出× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2015 | 1950 |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | James Durbin & Geoffrey Watson |
| 種類≠ | Univariate time-series model | Test for first-order residual autocorrelation |
| 原典≠ | 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 | Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428. DOI ↗ |
| 別名 | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi |
| 関連≠ | 5 | 4 |
| 概要≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations. |
| ScholarGateデータセット ↗ |
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