Underlying Core Inflation with Multiple Regimes
多状态下的潜在核心通胀AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以价格共同因子构建核心通胀，明确引入结构突变和状态转换，并评估实时预测表现。
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多状态下的潜在核心通胀
- 研究问题
- 如何在价格共同因子存在结构突变或状态转换时构建核心通胀指标，并兼顾历史修订、实时监测与总体通胀预测？
- 方法与思路
- 作者在高维因子模型中分别引入多重结构突变和马尔可夫转换，利用加拿大价格数据比较预测表现及指标修订程度。
- 材料陈述的贡献
- 摘要称，突变方案可避免既往状态的修订但只能离线使用；转换方案适于实时监测，并在通胀大幅变化期改善预测和修订。
- 阅读关注点
- 应核对状态变化与因子变化的设定、实时信息集及数据版本、修订指标，以及预测优势是否集中于特定通胀阶段。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未报告样本划分、比较数值或显著性细节，应用结论有情境限制。
原文摘要
This paper introduces a new approach for estimating core inflation indicators based on common factors across a broad range of price indices. Specifically, by utilizing procedures for detecting multiple regimes in high-dimensional factor models, we propose two types of core inflation indicators: one incorporating multiple structural breaks and another based on Markov switching. The structural breaks approach can eliminate revisions for past regimes, though it functions as an offline indicator, as real-time detection of breaks is not feasible with this method. On the other hand, the Markov switching approach can reduce revisions while being useful in real time, making it a simple and robust core inflation indicator suitable for real-time monitoring and as a short-term guide for monetary policy. Additionally, this approach allows us to estimate the probability of being in different inflationary regimes. To demonstrate the effectiveness of these indicators, we apply them to Canadian price data. To compare the real-time performance of the Markov switching approach to the benchmark model without regime-switching, we assess their abilities to forecast headline inflation and minimize revisions. We find that the Markov switching model delivers superior predictive accuracy and significantly reduces revisions during periods of substantial inflation changes. Hence, our findings suggest that accounting for time-varying factors and parameters enhances inflation signal accuracy and reduces data requirements, especially following sudden economic shifts.