Maximally Forward-Looking Core Inflation
最大化前瞻性的核心通胀指标AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以预测未来总体通胀为指标构造目标，并开展美欧样本外预测实验，直接对应宏观预测方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
最大化前瞻性的核心通胀指标
- 研究问题
- 如何构造能及时预示未来总体通胀的核心指标，并利用价格分项及价格变动分布提高中期通胀预测能力？
- 方法与思路
- 作者提出广义非负岭回归以优化价格分项权重；按每期分项排名重新组织输入，形成监督式截尾通胀指标。
- 材料陈述的贡献
- 摘要称，美欧样本外实验显示中期通胀信号改善，监督式截尾版本尤其突出，并讨论地区组合与上下行风险专用指标。
- 阅读关注点
- 核对预测期限、实时数据可得性、权重训练与更新规则，以及疫情前后比较基准和非对称截尾的稳健性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未提供误差数值与实时预测细节，不能据此确认政策决策收益。
原文摘要
Timely monetary policy decision-making requires timely core inflation measures. We create a new core inflation series that is explicitly designed to succeed at that goal. Precisely, we introduce the Assemblage Regression, a generalized nonnegative ridge regression problem that optimizes the price index's subcomponent weights such that the aggregate is maximally predictive of future headline inflation. Ordering subcomponents according to their rank in each period switches the algorithm to be learning supervised trimmed inflation - or, put differently, the maximally forward-looking summary statistic of the realized price changes distribution. In an extensive out-of-sample forecasting experiment for the US and the euro area, we find substantial improvements for signaling medium-term inflation developments in both the pre- and post-Covid years. Those coming from the supervised trimmed version are particularly striking, and are attributable to a highly asymmetric trimming which contrasts with conventional indicators. We also find that this metric was indicating first upward pressures on inflation as early as mid-2020 and quickly captured the turning point in 2022. We also consider extensions, like assembling inflation from geographical regions, trimmed temporal aggregation, and building core measures specialized for either upside or downside inflation risks.