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| Dynamisches Faktormodell× | Vektorautoregressionsmodell (VAR)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2002 | 2005 |
| Urheber≠ | James Stock & Mark Watson | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Typ≠ | Latent-factor time-series model | Multivariate time-series model |
| Wegweisende Quelle≠ | Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Aliasnamen | Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Verwandt≠ | 2 | 4 |
| Zusammenfassung≠ | 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. | 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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