Revisiting Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech
通过提出一种无监督语言模型，复现并扩展了GST对1981-2017年美国国会演讲中党派分化趋势的分析，揭示了主题内容的演变和关键短语。
ABSTRACT Gentzkow, Shapiro, and Taddy, Econometrica 87, no. 4, 2019 (henceforth GST), use a supervised text‐based regression model to assess changes in partisanship in US congressional speech over time. Their estimates imply that partisanship is far greater in recent years than in the past and that it increased sharply in the early 1990s. The paper at hand provides a replication in the wide sense of GST by complementing their analysis in three ways. First, we propose an alternative unsupervised language model, which combines ideas of topic models and ideal point models, to analyze the change in partisanship over time. We apply this model to the Senate speech data used in GST ranging from 1981 to 2017. Using our model, we replicate their results on the specific evolution of partisanship. Second, our model provides additional insights such as the data‐driven estimation of evolvement of topical contents over time. Third, we identify key phrases of partisanship on topic level.