  We adaptively estimate both changepoints and local outlier processes in a
Bayesian dynamic linear model with global-local shrinkage priors in a novel
model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We utilize a
state-space approach to identify a dynamic signal in the presence of outliers
and measurement error with stochastic volatility. We find that global state
equation parameters are inadequate for most real applications and we include
local parameters to track noise at each time-step. This setup provides a
flexible framework to detect unspecified changepoints in complex series, such
as those with large interruptions in local trends, with robustness to outliers
and heteroskedastic noise. Finally, we compare our algorithm against several
alternatives to demonstrate its efficacy in diverse simulation scenarios and
two empirical examples on the U.S. economy.
