TY - Journal Article
T1 - Bayesian compressed vector autoregressions
JF - Journal of econometrics
VL - 210
IS - 1
SP - 135
EP - 154
A1 - Koop, Gary
A2 - Korobilis, Dimitris
A2 - Pettenuzzo, Davide
PB - Elsevier B.V
PY - 2019
UR - https://bonnus.ulb.uni-bonn.de/SummonRecord/FETCH-LOGICAL-c3390-f776adb20484689dca3792a105b61ac1775c1732ebdd36eb5741a26a0f50bcc0
N2 - Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage.
KW - Forecasting
KW - Multivariate time series
KW - Random projection
KW - C53
KW - C11
KW - Multivariate time series
KW - Random projection
KW - C32
KW - Forecasting
ER -