Bayesian inference and forecasting in the stationary bilinear model
Bayesian inference and forecasting in the stationary bilinear model
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DOI：
10.1080/03610926.2016.1235193
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
R. León-González;Fuyu Yang
中科院分区：
文献类型：
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作者：
R. León-González;Fuyu Yang
摘要平稳双线性(SB)模型可以用来描述具有依赖于过去冲击的时变持续程度的过程。本研究发展了在SB模型中进行贝叶斯推断、模型比较和预测的方法。利用英国的月度通货膨胀数据，我们发现SB模型在均方根预测误差方面优于随机游走、一阶自回归AR(1)和自回归移动平均ARMA(1，1)模型。此外，SB模型对大多数预报观测值的预测似然比这三个模型更好。
ABSTRACT A stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using monthly U.K. inflation data, we find that the SB model outperforms the random walk, first-order autoregressive AR(1), and autoregressive moving average ARMA(1,1) models in terms of root mean squared forecast errors. In addition, the SB model is superior to these three models in terms of predictive likelihood for the majority of forecast observations.