Real-Time Inflation Forecasting in a Changing World
变化环境下的实时通胀预测AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究实时通胀预测，并在回归参数和误差方差中引入结构突变。
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变化环境下的实时通胀预测
- 研究问题
- 通胀预测同时面对预测变量选择不确定性和结构变化时，如何结合经济活动与预期信息，改善实时点预测和密度预测？
- 方法与思路
- 作者在多种回归设定间进行贝叶斯模型平均，允许各回归参数或整体误差方差随机突变，并评估美国两类通胀指标。
- 材料陈述的贡献
- 摘要称至少允许误差方差突变的多预测变量平均模型具有较高预测精度，尤其在1984年后，并有助于预测中期低通胀概率。
- 阅读关注点
- 应核对实时数据版本、预测变量可用时点、突变机制和先验设定，以及点预测、密度预测和低通胀事件的评价标准。
- 阅读边界
- 仅依据OpenAlex摘要；具体基准、预测期限及优势幅度未说明，不能判断所有结构变化设定均有效，也未提供补充材料内容。
原文摘要
This article revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, (relative) price data, and surveys. In this model average, we can entertain different channels of structural instability, by either incorporating stochastic breaks in the regression parameters of each individual specification within this average, or allowing for breaks in the error variance of the overall model average, or both. Thus, our framework simultaneously addresses structural change and model uncertainty that would unavoidably affect any inflation forecast model. The different versions of our framework are used to model U.S. personal consumption expenditures (PCE) deflator and gross domestic product (GDP) deflator inflation rates for the 1960–2011 period. A real-time inflation forecast evaluation shows that averaging over many predictors in a model that at least allows for structural breaks in the error variance results in very accurate point and density forecasts, especially for the post-1984 period. Our framework is especially useful when forecasting, in real-time, the likelihood of lower-than-usual inflation rates over the medium term. This article has online supplementary materials.