Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Faktoru papildinātais vektorautoregresijas modelis (FAVAR)× | Vektora autoregresijas (VAR) modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 2005 | 2005 |
| Autors≠ | Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Tips | Multivariate time-series model | Multivariate time-series model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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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