论文标题
一种贝叶斯时变的自回归模型，可改善短期和长期预测
A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction
论文作者
论文摘要
通过对德国利率的应用，我们提出了一个时间变化的自回归模型，用于短期和长期预测的时间序列，该模型表现出暂时的非平稳行为，但假定在长远来看意味着恢复。我们使用贝叶斯公式在模型中纳入了对平均恢复过程的先前假设，从而将未来的预测正常化。我们通过得出相关的完整有条件分布并在Gibbs采样器中使用大都市杂货来使用基于MCMC的推断来从后验（预测）分布中采样。在将数据驱动的短期预测与长期分布假设相结合时，我们的模型在短期内与现有方法具有竞争力，同时从长远来看得出了合理的预测。我们将模型应用于利率数据，并将预测性能与2 addive-Faltor高斯模型之一以及动态Nelson-Siegel模型的预测进行对比。
Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs Sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short term predictions with long term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to the one of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model.
