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| Faktoraugmenterad vektorautoregression (FAVAR)× | Vanligaste minsta kvadratmetoden (OLS) Regression× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2005 | 2019 |
| Upphovsperson≠ | Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Multivariate time-series model | Linear regression |
| 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Närliggande≠ | 4 | 5 |
| 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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