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首页> 外文期刊>Journal of applied statistics >An Interest-rate Model Analysis Based on Data Augmentation Bayesian Forecasting
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An Interest-rate Model Analysis Based on Data Augmentation Bayesian Forecasting

机译：基于数据增强贝叶斯预测的利率模型分析

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摘要

In this paper, the author presents an efficient method of analyzing an interest-rate model using a new approach called 'data augmentation Bayesian forecasting.' First, a dynamic linear model estimation was constructed with a hierarchically-incorporated model. Next, an observational replication was generated based on the one-step forecast distribution derived from the model. A Markov-chain Monte Carlo sampling method was conducted on it as a new observation and unknown parameters were estimated. At that time, the EM algorithm was applied to establish initial values of unknown parameters while the 'quasi Bayes factor' was used to appreciate parameter candidates. 'Data augmentation Bayesian forecasting' is a method of evaluating the transition and history of 'future,' 'present' and 'past' of an arbitrary stochastic process by which an appropriate evaluation is conducted based on the probability measure that has been sequentially modified with additional information." It would be possible to use future prediction results for modifying the model to grasp the present state or re-evaluate the past state. It would be also possible to raise the degree of precision in predicting the future through the modification of the present and the past. Thus, 'data augmentation Bayesian forecasting' is applicable not only in the field of financial data analysis but also in forecasting and controlling the stochastic process.
机译：在本文中，作者提出了一种使用称为“数据增强贝叶斯预测”的新方法来分析利率模型的有效方法。首先，使用层次合并模型构建动态线性模型估计。接下来，基于从模型导出的一步预测分布生成观察性复制。对其进行了马尔可夫链蒙特卡洛采样方法作为新的观测值，并估计了未知参数。当时，使用EM算法来建立未知参数的初始值，而使用“准贝叶斯因子”来欣赏候选参数。 “数据增强贝叶斯预测”是一种评估任意随机过程的“未来”，“当前”和“过去”的过渡和历史的方法，通过该过程，可以基于已被依次修改的概率测度进行适当的评估。可以使用将来的预测结果来修改模型，以掌握当前状态或重新评估过去的状态。也可以通过修改模型来提高预测未来的精确度。因此，“数据增强贝叶斯预测”不仅适用于金融数据分析领域，而且适用于预测和控制随机过程。

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