Large Bayesian VARs

By Martha Banbura, Domenico Giannone, L. Reichlin. Social Science Research Network, 2008

263 citations recorded by the source corpus.

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/24173f09a2e62aae6549cc87c3e66723bbe318f4

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In Economics and Econometrics

Citations in the corpus

Comparing the forecasting ability of financial conditions indices: The case of South Africa (2018, unknown): “all variables, including the first own lag (Banbura et al., 2010).”
Bayesian Mixed Frequency VARs (2015, methodology): “…the literature on monetary policy shocks, papers such as Christiano et al. (1996), Christiano et al. (1999), Sims and Zha (2006a), Sims and Zha (2006b), and Banbura et al. (2010), ignore the high frequency movements in interest rates and financial variables and estimate monthly or quarterly VARs.”
A comparison of forecast performance between federal reserve staff forecasts, simple reduced‐form models, and a DSGE model (2010, methodology): “Given the relatively large size of our system, our BVAR follows the approach of Banbura et al. [2008] for the specification of the prior distribution over parameters.”