Large Bayesian vector auto regressions

By Domenico Giannone, Martha Banbura, L. Reichlin. 2010

1,215 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ae47336de60524df45e540683188ffea79ff5510

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In Economics and Econometrics

Citations in the corpus

Likelihood-based Analysis for Dynamic Factor Models (2008, methodology): “to produce comparable results in forecast precision as for a dynamic factor analysis; see Banbura et al. (2010). The presented results below can be used jointly with a shrinkage method in a BVAR analysis.”
Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data (2010, methodology): “This type of models implies zero restriction on some factor loadings and/or autoregressive parameters in the factor VAR, which can be imposed either by using the formula (13) or by estimating each block of Λ or A separately (see Bańbura, Giannone, and Reichlin, 2010b).”
Now-Casting and the Real-time Data Flow (2013, neutral): “…not deteriorate the performance of the model, supporting the results on the robustness of the factor model to data selection (see the empirical analysis in Bańbura, Giannone, and Reichlin (2010) for longer horizons forecasting and the simulation study of Doz, Giannone, and Reichlin (2011, 2012)).”
Forecasting oil and stock returns with a Qual VAR using over 150 years of data (2017, methodology): “Following the suggestions of Banbura et al., (2010), we accommodate the problem of overparametrization (often leading to poor forecasts) in the VAR and the Qual VAR compared to the standard AR by using Bayesian shrinkage to ensure that all these models”
Macroeconomic forecast accuracy in a data‐rich environment (2019, neutral): “See Banbura et al. (2010), Koop (2013), Carriero et al. (2015) and Giannone et al. (2015), among others.”
Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012, methodology): “The model is estimated as in Bańbura et al. (2009), imposing a Minnesota prior on the transformed series, which are all assumed to be stationary.”
Real-Time Forecasting With a Mixed-Frequency VAR (2015, neutral): “…assessment and Giannone, Lenza, and Primiceri 2014 for a recent assessment) and there is strong evidence that they perform well in high-dimensional environments if estimated with shrinkage estimation techniques (see, e.g., De Mol, Giannone, and Reichlin 2008; Banbura, Giannone, and Reichlin 2010).”
Large time-varying parameter VARs (2013, methodology): “Many recent papers (see, among many others, Banbura et al., 2010; Carriero et al., 2011, 2009;Giannone et al., 2010; Koop, 2013) have found large VARs, which have dozens or even hundreds of dependent variables, to forecast well.1 In this literature, the researcher typically works with a single large”
Common Drifting Volatility in Large Bayesian VARs (2012, neutral): “(16) For this model, we follow other work on large VARs (e.g., Carriero, Kapetanios, and Marcellino 2009; Banbura, Giannone, and Reichlin 2010) and use the Normal-inverted Wishart prior and posterior detailed in such studies as Kadiyala and Karlsson (1997).”
Prior Selection for Vector Autoregressions (2015, methodology): “As for the second method, we have replicated Bańbura et al. (2010) by setting the hyperparameters in the medium and large BVARs to match the average in-sample fit of the small VAR with flat priors.8 Table 4 reports the MSFE of the Litterman (1980) (LIT) and Bańbura et al. (2010) (BGR) methods…”
Panel Vector Autoregressive Models: A Survey (2013, methodology): “This section sketches the main features of large scale Bayesian VARs (e.g. Banbura et al, 2010), spatial econometric models (see Anselin, 2010), factor models (see e.g. Stock and Watson, 1989, 2003), global VARs (see Dees et al., 2007 and Pesaran, et al., 2004) and bilateral panel VARs (see e.g.…”