Forecasting with Bayesian Vector Autoregression

By Sune Karlsson. 2013

189 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7f20124ae37cffc621fae5d8b59493a23b122d8f

How researchers see this paper

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

In Economics and Econometrics

1. M. Lenza
2. Domenico Giannone
3. Giorgio E. Primiceri
4. Lijun Sun
5. Xinyu Chen
6. Gary Koop
7. Dimitris Korobilis
8. Christiane Baumeister

In Finance

1. Domenico Giannone
2. M. Lenza
3. Lijun Sun
4. Xinyu Chen
5. Giorgio E. Primiceri
6. Mark F. J. Steel
7. L. Reichlin
8. Mark W. Watson

In Management Science and Operations Research

1. M. Lenza
2. Giorgio E. Primiceri
3. Lijun Sun
4. Domenico Giannone
5. Xinyu Chen
6. K. Petrova
7. Christiane Baumeister
8. Lutz Kilian

Citations in the corpus

• Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections (2015, unknown): “See Karlsson (2013) for a comprehensive overview of Bayesian methods for inference and forecasting with VAR models.”
• Common Drifting Volatility in Large Bayesian VARs (2012, unknown): “3.2.1 of Karlsson 2013), without cross-variable shrinkage: μ = 0, such that E[ (ij )l ] = 0 ∀ i, j, l (34) 0 such that the entry corresponding to (ij ) l = { θ2 l2σ 2j for l > 0 ε2 for l = 0 .”
• Now-Casting and the Real-time Data Flow (2013, unknown): “Either the forecasts from many smaller systems could be combined or Bayesian shrinkage could be employed to avoid over-fitting in a large system, see Bańbura, Giannone, and Reichlin (2010), Giannone, Lenza, and Primiceri (2012) or Koop (2013) (see also Karlsson, 2012).”
• Bayesian Temporal Factorization for Multidimensional Time Series Prediction (2019, methodology): “We follow TRMF [19] and employ VAR process to characterize the temporal dependencies in X (see (3)), where VAR is indeed more flexible and its Bayesian model [34]—BVAR—can be adapted to Bayesian matrix/tensor factorization naturally.”
• Real-Time Forecasting With a Mixed-Frequency VAR (2015, unknown): “Alternative prior specifications for Bayesian VARs are surveyed in Karlsson (2013) and the effect of specification choice on forecast accuracy is studied in Carriero, Clark, and Marcellino (2013).”
• Prior Selection for Vector Autoregressions (2015, methodology): “First, this class includes the priors most commonly used by the literature on BVARs (see the surveys of Koop & Korobilis, 2010; Del Negro & Schorfheide, 2011; Karlsson, 2012).”