News-driven inflation expectations and information rigidities
利用新闻语料和机器学习，发现媒体报道的主题能预测通胀和通胀预期，并解释家庭信息刚性的时变特征，为理解媒体在预期形成中的作用提供新证据。
Using a large news corpus and machine learning algorithms we investigate the role played by the media in the expectations formation process of households, and conclude that the news topics media report on are good predictors of both inflation and inflation expectations. In turn, in a noisy information model, augmented with a simple media channel, we document that the time series features of relevant topics help explain time-varying information rigidity among households. As such, we provide a novel estimate of state-dependent information rigidities and present new evidence highlighting the role of the media in understanding inflation expectations and information rigidities.