Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices
From MaRDI portal
Publication:5077392
DOI10.1080/03610926.2019.1565832OpenAlexW4252915822WikidataQ128105693 ScholiaQ128105693MaRDI QIDQ5077392
Publication date: 18 May 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10292/12258
Cites Work
- Unnamed Item
- Unnamed Item
- Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
- Modelling and forecasting government bond spreads in the euro area: a GVAR model
- The Oxford Handbook of Bayesian Econometrics
- Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference
- Some matrix-variate distribution theory: Notational considerations and a Bayesian application
- The mle algorithm for the matrix normal distribution
- STOCK RETURNS, TERM STRUCTURE, INFLATION, AND REAL ACTIVITY: AN INTERNATIONAL PERSPECTIVE
- Forecasting and conditional projection using realistic prior distributions
This page was built for publication: Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices