Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy in terms of the methods, with approaches that do not approximate the states yielding superior accuracy over methods that do. We also document numerically that the inferential discrepancies between the various methods often yield only small discrepancies in predictive accuracy over small out-of-sample evaluation periods. Nevertheless, in certain settings, these predictive discrepancies can become marked over longer out-of-sample periods. This finding indicates that the invariance of predictive results to inferential inaccuracy, which has been an oft-touted point made by practitioners seeking to justify the use of variational inference, is not ubiquitous and must be assessed on a case-by-case basis.
翻译:使用理论和数字结果,我们记录一系列国家空间模型中通用的变异海湾方法的准确性。结果显示,就固定参数的准确性而言,在方法上存在着明确的等级分级,其方法不近于国家产生优于方法的准确性。我们还用数字来记录,不同方法之间的推论差异往往在小型抽样评估期间仅产生少量的预测准确性差异。然而,在某些情况下,这些预测性差异可能会在较长的抽样期间出现。这一发现表明,那些试图证明使用变异推断理由的从业人员经常指出的推断不准确性的预测结果的偏差,不是无处不在的,必须逐案评估。