  Variational Bayes methods are a potential scalable estimation approach for
state space models. However, existing methods are inaccurate or computationally
infeasible for many state space models. This paper proposes a variational
approximation that is accurate and fast for any model with a closed-form
measurement density function and a state transition distribution within the
exponential family of distributions. We show that our method can accurately and
quickly estimate a multivariate Skellam stochastic volatility model with
high-frequency tick-by-tick discrete price changes of four stocks, and a
time-varying parameter vector autoregression with a stochastic volatility model
using eight macroeconomic variables.
