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.
翻译:动态系数模型往往通过点估计方法估计,而忽略参数不确定性。我们建议一种方法,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,包括不同的抽样大小和混合频率。我们的方法还产生一种直向估算算法,没有耗时的模拟技术。在使用大小模型的经验实例中,我们用我们的方法与MMC模拟模型中的全部巴伊西亚估计进行比较。一般而言,近似捕捉系数特征和参数很好,并带来巨大的计算收益。由此得出的预测分布在微小的计算时间内,接近非常精确,几乎无法与MCMC的样本中外分布相区别。