论文标题
具有任意丢失数据的动态因子模型的变异推断
Variational Inference of Dynamic Factor Models with Arbitrary Missing Data
论文作者
论文摘要
动态因子模型通常是通过点估计方法估算的，无视参数不确定性。我们使用变分推断提出了一种通过后近似值来考虑参数不确定性的方法。我们的方法允许任何丢失数据的任意模式，包括不同的样本量和混合频率。它还产生直接的估计算法，没有耗时的仿真技术。在使用小型和大型模型的经验示例中，我们将我们的方法与MCMC模拟中的完整贝叶斯估计进行了比较。通常，近似值可以很好地捕获因子特征和参数，并具有巨大的计算收益。所得的预测分布近似于很高的精度，在一小部分计算时间中，几乎与样品中的MCMC几乎没有区别。
Dynamic factor models are often estimated by point-estimation methods, disregarding parameter uncertainty. We propose a method accounting for parameter uncertainty by means of posterior approximation, using variational inference. Our approach allows for any arbitrary pattern of missing data, including different sample sizes and mixed frequencies. It also yields a straight-forward estimation algorithm absent of time-consuming simulation techniques. In empirical examples using both small and large models, we compare our method to full Bayesian estimation from MCMC-simulations. Generally, the approximation captures factor features and parameters well, with vast computational gains. The resulting predictive distributions are approximated to a very high precision, almost indistinguishable from MCMC both in and out of sample, in a tiny fraction of computational time.
