Principal components at work: the empirical analysis of monetary policy with large data sets

By Carlo A. Favero, Massimiliano Marcellino, F. Neglia. 2005

176 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c8095ae3eae461cdfac0ca6ef0ff993f1b948031

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In Economics and Econometrics

Citations in the corpus

A parametric estimation method for dynamic factor models of large dimensions (2009, background): “Stock and Watson (2001, 2002) showed that the first two Stock and Watson (2001, 2002) factors are the most relevant for forecasting several variables in the data set, while Favero et al. (2005) found that three or four Forni et al. (1999, 2000) factors are sufficient.”
DYNAMIC FACTOR MODELS (2018, methodology): “Empirical applications in which estimated factors are used as instruments include Favero, Marcellino, and Neglia (2005) and Beyer, Farmer, Henry, and Marcellino (2008).”
Dynamic factor models with infinite-dimensional factor spaces: One-sided representations (2015, background): “, 2010), (c) structural macroeconomic analysis and monetary policy (Bernanke and Boivin, 2003; Bernanke et al., 2005; Stock andWatson, 2005; Giannone et al., 2005; Favero et al., 2005; Eickmeier, 2007; Forni et al., 2009; Boivin et al., 2009; Forni and Gambetti, 2010), (d) the analysis of financialm”
Bayesian VARs with large panels (2007, background): “The empirical relevance of taking into account such information has been shown by Bernanke, Boivin, and Eliasz (2005), Favero, Marcellino, and Neglia (2005), Giannone, Reichlin, and Sala (2004) and Stock and Watson (2005b) in frameworks related to factor analysis.”
Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy (2014, unknown): “Favero et al. (2005) find homogeneous effects of monetary policy shocks on output gaps and inflation rates, while McCallum and Smets (2009) find heterogeneous responses in terms of real wage.”
Determining the number of factors in approximate factor models with global and group-specific factors (2008, unknown): “[12] Favero, C.”
Forecasting with Many Predictors (2006, methodology): “A point stressed by Favero, Marcellino and Neglia (2002) is that the dynamic PCA methods estimate the factors by a two-sided filter, which makes it problematic, or even unsuitable, for applications in which strict timing is important, such as using the estimated factors in VARs or as instrumental…”
Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure (2019, unknown): “In macroeconomic panels the unobserved factors are frequently viewed as economy-wide shocks, affecting all individuals albeit with different intensities; see e.g. Favero et al. (2005).”
EFFICIENT ESTIMATION OF FACTOR MODELS (2011, unknown): “…1999, 2002b; Artis, Banerjee, and Marcellino, 2005; Forni, Hallin, Lippi, and Reichlin, 2003; Huang, Lee, and Li, 2006), policy analysis (e.g., Favero, Marcellino, and Neglia, 2005; Bernanke, Boivin, and Eliasz, 2005), predictive regressions (e.g., Ludvigson and Ng, 2007), and instrumental…”
Forecasting the Yield Curve in a Data-Rich Environment: A No-Arbitrage Factor-Augmented VAR Approach (2008, unknown): “Examples for such models are Hördahl et al. (2006), Diebold et al.”