The relationship between forecast dispersion and forecast uncertainty: Evidence from a survey data—arch model
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预测分歧与预测不确定性的关系：调查数据与 ARCH 模型证据
- 研究问题
- 通胀调查中受访者预测的分歧，是否与预测不确定性相关，并能否作为不同调查系列中通胀不确定性的可靠代理？
- 方法与思路
- 作者用 ARCH 刻画调查预测误差的条件方差，处理重叠预测期限带来的约束，并通过 GMM 应对序列相关和条件异方差。
- 材料陈述的贡献
- 摘要称预测分歧通常与估计的通胀不确定性显著正相关，但将分歧作为不确定性代理是否合适，取决于所选调查系列。
- 阅读关注点
- 核对预测期限重叠如何限制 ARCH 设定、GMM 矩条件是否有效，以及跨受访者分歧与预测误差方差的概念差别。
- 阅读边界
- 仅据所给 Crossref 摘要，不确定性由模型推导而非概率预测直接提供；具体关联强度和稳健性未说明，也不能作因果解释。
原文摘要
Abstract This paper examines empirically the relationship between measures of forecast dispersion and forecast uncertainty from data on inflation expectations from the Livingston survey series and the Survey Research Center (SRC) survey series. Because the survey series do not provide probabilistic forecasts of inflation, we derive measures of inflation uncertainty by modelling the conditional variance of the inflation forecast errors from the survey series as an autoregressive conditional heteroscedastic (ARCH) process. The analysis is complicated by the fact that the overlap of forecast horizons for the survey series does not preclude the model's disturbance terms from displaying autocorrelation, and also places a restriction on the specification for the ARCH measures of inflation uncertainty. We estimate the model using Hansen's (1982) generalized method of moments (GMM) procedure to account for the presence of serial correlation and conditional heteroscedasticity in the disturbance terms. The results generally support the hypothesis that the measures of forecast dispersion across survey respondents are positively and statistically significantly associated with the measures of inflation uncertainty. However, the appropriateness of using forecast dispersion measures as proxies for inflation uncertainty is sensitive to the choice of the survey series.