POSTERIOR‐PREDICTIVE EVIDENCE ON US INFLATION USING EXTENDED NEW KEYNESIAN PHILLIPS CURVE MODELS WITH NON‐FILTERED DATA
使用未滤波数据的扩展新凯恩斯菲利普斯曲线：美国通胀的后验预测证据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。
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：摘要明确建模美国通胀的时变时间序列性质，并比较后验预测与完整预测分布。
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…
使用未滤波数据的扩展新凯恩斯菲利普斯曲线：美国通胀的后验预测证据
- 研究问题
- 研究美国通胀与边际成本的时变水平和波动如何建模，以及预先去除低频变化与不同预期机制如何影响拟合和预测。
- 方法与思路
- 作者将结构时间序列成分加入新凯恩斯菲利普斯曲线，纳入前瞻、后顾预期和调查预期信息，使用模拟式贝叶斯方法分析。
- 材料陈述的贡献
- 摘要称，后顾成分较强，丰富模型更精确估计水平和波动，拟合与预测比较有利；未发现内生性及长期稳定关系的可信证据。
- 阅读关注点
- 应核对未滤波数据的处理、预期信息进入似然的方式，以及内生性与稳定性检验、预测评分和通缩尾部概率的样本时期。
- 阅读边界
- 摘要未报告模型细节或预测数值，缺乏可信证据不等于证明不存在相关机制；其“近年”通缩概率陈述不能视为当前判断。
原文摘要
SUMMARY Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns in levels and volatilities. Forward‐ and backward‐looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation‐based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long‐run stability between inflation and marginal costs. Backward‐looking inflation appears stronger than forward‐looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years. Copyright © 2014 John Wiley & Sons, Ltd.