手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| MIDAS回帰：混合データ頻度を跨いだ予測× | ARIMA（自己回帰和分移動平均）モデル× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2007 | 2015 |
| 提唱者≠ | Eric Ghysels, Arthur Sinko & Rossen Valkanov | Box & Jenkins (Box-Jenkins methodology) |
| 種類≠ | Parametric mixed-frequency forecasting model | Univariate time-series model |
| 原典≠ | Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90. DOI ↗ | 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 |
| 別名≠ | Mixed Frequency Regression, Mixed Data Sampling Model, High-Frequency Forecasting Regression, MIDAS Regresyonu | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| 関連≠ | 3 | 5 |
| 概要≠ | MIDAS (Mixed Data Sampling) Regression is an econometric framework that directly incorporates high-frequency predictors into models for lower-frequency outcome variables without requiring temporal aggregation of the regressors. Introduced by Eric Ghysels, Arthur Sinko, and Rossen Valkanov in 2007, MIDAS uses parsimoniously parameterized lag polynomials — such as the Beta or Exponential Almon weighting schemes — to summarize the information content of many high-frequency lags while avoiding parameter proliferation. | 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). |
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