概率通胀预测的序贯有效推断
Sequentially valid inference for probabilistic inflation forecasts
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中文总结 AI 辅助
本研究针对概率预测校准的序贯评估难题，提出基于e-value的序贯检验方法，将其应用于美、欧元区、瑞士的通胀预测，可捕捉静态检验遗漏的结构断裂期校准问题，为宏观经济预测校准评估提供实用方法。
中文摘要 AI 辅助
传统统计检验不适用于概率预测校准的序贯评估。我们将一种基于e-value的新型序贯检验方法应用于宏观经济预测，以解决这一局限。基于e-value的方法可实现任意时刻有效的推断，使从业者能够持续对校准情况进行检验，而不会失效统计保证。为说明该框架的实用价值，我们将其应用于美国、欧元区和瑞士的概率通胀预测。分析显示，序贯方法能提供关于预测误设定的时间和性质的详细洞察，这些诊断在重大结构断裂期间尤其具有启发性；在此类事件中，我们发现了反对校准的证据，而静态全样本检验常常会遗漏这些证据。因此，本研究表明基于e-value的检验是实证宏观经济学中评估预测校准的实用方法。
英文摘要
Traditional statistical tests are poorly suited for the sequential evaluation of probabilistic forecast calibration. We address this limitation in macroeconomic forecasting by applying a new sequential testing method based on e-values. The e-value-based methodology enables anytime-valid inference. It allows practitioners to test against calibration continuously without invalidating statistical guarantees. To illustrate the framework's practical value, we apply it to probabilistic inflation forecasts for the United States, the Euro Area, and Switzerland. Our analysis shows that the sequential approach gives detailed insights into the timing and nature of forecast misspecification. We find these diagnostics are particularly insightful during major structural breaks. During these events, we find evidence against calibration that static, full-sample tests often miss. Therefore, this work shows that e-value-based tests are a practical method for the evaluation of forecast calibration in empirical macroeconomics.
