  This paper proposes a straightforward algorithm to carry out inference in
large time-varying parameter vector autoregressions (TVP-VARs) with mixture
innovation components for each coefficient in the system. We significantly
decrease the computational burden by approximating the latent indicators that
drive the time-variation in the coefficients with a latent threshold process
that depends on the absolute size of the shocks. The merits of our approach are
illustrated with two applications. First, we forecast the US term structure of
interest rates and demonstrate forecast gains of the proposed mixture
innovation model relative to other benchmark models. Second, we apply our
approach to US macroeconomic data and find significant evidence for
time-varying effects of a monetary policy tightening.
