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| MIDAS回帰：混合データ頻度を跨いだ予測× | 動的因子モデル× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2007 | 2002 |
| 提唱者≠ | Eric Ghysels, Arthur Sinko & Rossen Valkanov | James Stock & Mark Watson |
| 種類≠ | Parametric mixed-frequency forecasting model | Latent-factor 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 ↗ | Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162. DOI ↗ |
| 別名 | Mixed Frequency Regression, Mixed Data Sampling Model, High-Frequency Forecasting Regression, MIDAS Regresyonu | Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli |
| 関連≠ | 3 | 2 |
| 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. | A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models. |
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