Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Векторная авторегрессия с добавлением факторов (FAVAR)× | Модель векторной авторегрессии (VAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления | 2005 | 2005 |
| Автор метода≠ | Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Тип | Multivariate time-series model | Multivariate time-series model |
| Основополагающий источник≠ | Bernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Другие названия≠ | factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Связанные | 4 | 4 |
| Сводка≠ | FAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once. | 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). |
| ScholarGateНабор данных ↗ |
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