Nber Working Paper Series Measuring the Effects of Monetary Policy : a Factor-augmented Vector Autoregressive ( Favar ) Approach

By Ben S. Bernanke, Jean Boivin, Piotr Eliasz. 2003

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Citations in the corpus

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, background): “, 2000; 2005) or factor-augmented vector autoregressive models (Bernanke et al., 2005).”
Sufficient Forecasting Using Factor Models (2015, background): “Typical examples include forecasts of a macroeconomic output using a large number of employment and production variables (Stock and Watson, 1989; Bernanke et al., 2005), forecasts of the values of market prices and dividends using cross-sectional asset returns (Sharpe, 1964; Lintner, 1965), and…”
Large Bayesian vector auto regressions (2010, methodology): “…a valid alternative to factor-based forecasts, at least to those based on the FAVAR method.4 We should also note that BIC lag selection generates the best results for the FAVAR, while the original specification of Bernanke et al. (2005) with p D 13 performs very poorly due to its lack of parsimony.”
Granger Causality: A Review and Recent Advances (2021, methodology): “Bernanke et al. (2005) state that “To conserve degrees of freedom, standard VARs rarely employ more than six to eight variables.””
In Search for Yield? Survey-Based Evidence on Bank Risk Taking (2014, background): “5 For a more thorough discussion of the empirical framework, see Bernanke et al. (2005) and Buch et al.”
Measuring Uncertainty and Its Impact on the Economy (2016, methodology): “This means that our identiﬁcation scheme is very similar to the one typical of factor-augmented VAR models, such as Bernanke, Boivin and Eliasz (2005).”
Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data (2010, methodology): “Restrictions on the parameters in the transition equation: HAvec(A) = κA, can be imposed in an analogous manner, see Bork (2009).15 This type of restrictions are relevant for a number of models, such as e.g.: • Factor Augmented VAR (FAVAR) models as proposed by Bernanke, Boivin, and Eliasz (2005).”
Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel (2007, methodology): “Notably, Forni, Hallin, Lippi and Reichlin (2000) suggest informal methods based on the relative size of eigenvalues from the estimated spectral density matrix for X, related methods have been proposed and applied in the empirical analysis of Forni, Lippi and Reichlin (2003), Giannone, Reichlin and ”
Maximum Likelihood Estimation and Inference for Approximate Factor Models of High Dimension (2016, unknown): “(See Bernanke & Boivin, 2003; Bernanke et al., 2005; Kose, Otrok, & Whiteman, 2003.)”
Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012, methodology): “In this sense, the adaptive (group) Lasso constitutes an alternative way of handling high-dimensionality in impulse response analysis than the factor augmented VAR (FAVAR) of Bernanke et al. (2005)(1).”