The Shape of Macroeconomic Beliefs
宏观经济信念的分布形态AI 解读 ↓
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宏观经济信念的分布形态
- 研究问题
- 预测市场价格能否揭示通胀预期的完整分布，并提供点预测遗漏的不确定性和高通胀风险信息，尤其在数据发布前？
- 方法与思路
- 作者将 Kalshi 相邻阈值合约价格转换为通胀结果的概率质量，构建发布前多时点分布，并分析其与历史预测意外的关系。
- 材料陈述的贡献
- 摘要称，历史正向通胀意外与更高尾部概率相关；发布层面检验显示，市场隐含尾部概率包含点预测之外的信息。
- 阅读关注点
- 应核对合约价格转换为概率的条件、市场流动性、固定效应设定，以及发布层面预测检验如何处理重复观测和样本划分。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要报告的是关联和预测信息，不能据此确认信念调整的因果机制。
原文摘要
Macroeconomic expectations are usually observed through point forecasts or through asset prices whose mapping into beliefs is model-dependent. This paper uses prediction-market prices to recover high-frequency distributions of short-run macroeconomic beliefs. We construct a panel of Kalshi-implied distributions for CPI and core CPI releases by converting adjacent threshold contracts into probability mass over inflation outcomes. The data reveal market-implied means, uncertainty, and upper-tail probabilities from 30 days to one hour before each release. The market-implied mean contains meaningful forecast information, especially for headline CPI, but the main signal is distributional. Lagged Reuters Poll surprises do not predict systematic deviations of Kalshi means from the current Reuters consensus. By contrast, large lagged surprises are associated with higher implied uncertainty, and positive lagged surprises raise the probability assigned to fixed high-inflation outcomes. In the baseline specification with variable-by-horizon fixed effects, a 0.1 percentage point positive lagged surprise raises the probability of monthly inflation above 0.3 percent by about 4.7 percentage points, even after controlling for the current consensus forecast. In release-level validation tests, Kalshi upper-tail probabilities also predict the realization of high-inflation states, including episodes in which the market-implied mean remains close to the Reuters consensus. The evidence suggests that prediction markets can provide real-time information about inflation risk that is missed by point forecasts.