Large Bayesian Matrix Autoregressions with Missing Data

Music City Center 

Large datasets of matrix-valued time-series are increasingly common in
economics and finance, but they are typically subjected to complex missing
data patterns, such as unbalanced panels and mixed-frequency settings.
We develop a data augmentation scheme that can handle a large number
of missing values and complex cross-sectional and temporal correlation
structures. We illustrate the methodology by producing model-based
estimates of US state-level quarterly GDP from 1988 using a large dataset
of over 400 time-series. These include state-level quarterly GDP data from
2005, state-level annual GDP data from 1997, as well as a wide range of
state-level macroeconomic variables from 1988.

Bayesian

matrix-valued time-series

GDP

Regional data