Uncertainty Over Models and Data: The Rise and Fall of American Inflation
研究经济主体在模型和数据不确定下如何学习，发现数据不确定性使学习过程更缓慢，从而解释了美国通胀的起落与感知的菲利普斯曲线权衡变化的关系。
Economic agents who are uncertain of their economic model learn, and this learning is sensitive to the presence of data uncertainty. I investigate this idea in a framework that successfully describes inflation as a learning Federal Reserve’s optimal policy but fails to satisfactorily motivate these policy shifts. I modify the framework to account for data uncertainty: the learning process is made more sluggish by its presence. Consequently, the estimated model provides an explanation for the rise and fall in inflation: the concurrent rise and fall in the perceived Philips curve trade‐off between inflation and unemployment.