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Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Faktoraugmenterad vektorautoregression (FAVAR)× | Vektorautoregressionsmodell (VAR)× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår | 2005 | 2005 |
| Upphovsperson≠ | Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Typ | Multivariate time-series model | Multivariate time-series model |
| Ursprungskälla≠ | 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 ↗ |
| Alias≠ | factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Närliggande | 4 | 4 |
| Sammanfattning≠ | 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). |
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