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Bayesian Vector Autoregressions
Author(s) -
Woźniak Tomasz
Publication year - 2016
Publication title -
australian economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 29
eISSN - 1467-8462
pISSN - 0004-9018
DOI - 10.1111/1467-8462.12179
Subject(s) - bayesian probability , benchmark (surveying) , posterior probability , computer science , econometrics , bayesian vector autoregression , bayes factor , bayesian inference , feature (linguistics) , bayes' theorem , bayesian linear regression , distribution (mathematics) , mathematical optimization , mathematics , artificial intelligence , mathematical analysis , linguistics , philosophy , geodesy , geography
This article provides an introduction to the burgeoning academic literature on Bayesian vector autoregressions, benchmark models for applied macroeconomic research. I first explain Bayes’ theorem and the derivation of the closed‐form solution for the posterior distribution of the parameters of the model's given data. I further consider parameter shrinkage, a distinguishing feature of the prior distributions commonly employed in the analysis of large data. Finally, I describe the mechanisms that enable feasible computations for these linear models that efficiently extract the information content of many variables for economic forecasting and other applications.