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.

翻译：变分贝叶斯方法是状态空间模型的一种具有扩展性的潜在估计方法。然而，现有方法对于许多状态空间模型要么精度不足，要么计算上不可行。本文提出了一种变分近似方法，该方法对于任何具有闭合形式测量密度函数且状态转移分布属于指数分布族的模型，都能实现精确且快速的估计。我们证明，该方法能够准确快速地估计包含四只股票高频逐笔离散价格变化的多变量Skellam随机波动率模型，以及使用八个宏观经济变量的时变参数向量自回归随机波动率模型。