论文标题
基于样本的贝叶斯预测算法的案例研究
A Case-Study of Sample-Based Bayesian Forecasting Algorithms
论文作者
论文摘要
对于贝叶斯，对后验预测分布的实时预测对于各种时间序列模型可能会具有挑战性。首先，当模型的可能性是棘手和/或使用数据集时，估算时间序列模型的参数可能很困难。其次，一旦在固定的数据窗口中获得了来自参数后验的样本，就不清楚它们将如何用于生成预测，也不清楚它们如何，从什么意义上讲，它们将被``更新''转移到新的后代，因为新的数据随着新数据的到来。非线性/非高斯国家空间模型。使用良好的随机波动率模型提供了对财务收益的分析。
For a Bayesian, real-time forecasting with the posterior predictive distribution can be challenging for a variety of time series models. First, estimating the parameters of a time series model can be difficult with sample-based approaches when the model's likelihood is intractable and/or when the data set being used is large. Second, once samples from a parameter posterior are obtained on a fixed window of data, it is not clear how they will be used to generate forecasts, nor is it clear how, and in what sense, they will be ``updated" as interest shifts to newer posteriors as new data arrive. This paper provides a comparison of the sample-based forecasting algorithms that are available for Bayesians interested in real-time forecasting with nonlinear/non-Gaussian state space models. An applied analysis of financial returns is provided using a well-established stochastic volatility model. The principal aim of this paper is to provide guidance on how to select one of these algorithms, and to describe a variety of benefits and pitfalls associated with each approach.
