Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy

113 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data (2010, methodology): “Belviso and Milani (2006) extract factors from blocks of variables representing a single concept (e.”
Shocking language: understanding the macroeconomic effects of central bank communication (2016, unknown): “Belviso and Milani (2006) estimate a ‘structural FAVAR ’ in which they actually identify specific titles for the factors.”
Expectation Shocks and Learning as Drivers of the Business Cycle (2011, unknown): “…be explained by a group of dynamic factors (F1,t, . . . , F7,t), which are extracted from a large set of macroeconomic variables and taken from Belviso and Milani (2006).19 The two regressions are shown below (SE in parenthesis): e y1 t ¼ ĉ þ 0:83 ð0:05Þ e y1 t 1 0:02ð0:05Þ Oil Pricet þ 0:03…”
Granger Causality: A Review and Recent Advances (2021, background): “Followup work has further investigated the estimability of the parameters (Belviso and Milani, 2006) and the choice of number of unobserved factors (Ahn and Horenstein, 2013; Onatski, 2010; Amengual and Watson, 2007).”
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (2010, unknown): “This is done in papers such as Bernanke, Boivin and Eliasz (2005) and Belviso and Milani (2006). The FAVAR modies a DFM such as (52) by adding other explanatory variables to the M measurement equations:”