论文标题
状态空间模型的有效变异近似
Efficient variational approximations for state space models
论文作者
论文摘要
变分贝叶斯方法是状态空间模型的潜在可扩展估计方法。但是，对于许多状态空间模型而言，现有方法是不准确或计算上不可行的。本文提出了一个变异近似，对于任何具有封闭形式的测量密度函数和指数分布家族中的状态过渡分布的模型，该变异近似是准确而快速的。我们表明，我们的方法可以准确，快速估计具有高频的四个股票的高频离散价格变化的多元Skellam随机波动率模型，以及使用八个宏观经济变量的随机波动性波动率模型的随时间变化的参数矢量自动化。
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.
