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。
| 構造的時系列モデル（基本構造モデル）× | ベクトル自己回帰（VAR）モデル× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1990 | 2005 |
| 提唱者≠ | Andrew C. Harvey | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| 種類≠ | State-space (unobserved components) time series model | Multivariate time-series model |
| 原典≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| 別名 | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| 関連 | 4 | 4 |
| 概要≠ | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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