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Bayesovská vektorová autoregresia (BVAR)×Faktorovo augmentovaná vektorová autoregresia (FAVAR)×
OdborEkonometriaEkonometria
RodinaRegression modelRegression model
Rok vzniku19862005
TvorcaLitterman (1986); Bańbura, Giannone & Reichlin (2010)Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes
TypBayesian multivariate time-series modelMultivariate time-series model
Pôvodný zdrojLitterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗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 ↗
Ďalšie názvyBVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)
Príbuzné54
ZhrnutieBayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.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.
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ScholarGatePorovnať metódy: Bayesian VAR · FAVAR. Získané 2026-06-17 z https://scholargate.app/sk/compare