Dynamic factor models are typically estimated by point-estimation methods, disregarding parameter uncertainty. We propose a new 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.
翻译:动态系数模型通常通过点估计方法估计,而忽略参数不确定性。我们建议采用新的方法,使用变式推论,以后近似法来计算参数不确定性。我们的方法允许任何任意的缺失数据模式,包括不同的样本大小和混合频率。我们的方法还产生直向估算算法,没有耗时的模拟技术。在使用小型和大型模型的经验性实例中,我们比较了我们的方法和从MCMC模拟中完全估算的Bayesian方法。一般而言,近似捕捉系数特征和参数很好,并取得了巨大的计算收益。由此得出的预测分布近似非常精确,几乎无法在微小的计算时间里与MCMC的样本内外的分布相区别。