论文标题
重新访问高维选择的小组差异：国会演讲的方法和应用
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
论文作者
论文摘要
Gentzkow，Shapiro和Taddy，《计量经济学》，第87卷，第4期，2019年（此后GST）使用有监督的基于文本的回归模型来评估随着时间的推移，美国国会演讲的党派变化。他们的估计表明，近年来党派远比过去要大得多，并且在1990年代初期，党派化幅度急剧增加。手头的论文通过以三种方式补充其分析，从而从广泛的GST意义上进行了复制。首先，我们提出了一种替代性无监督的语言模型，该模型结合了主题模型的思想和理想点模型，以分析党派随着时间的推移的变化。我们将此模型应用于GST中使用的参议院语音数据，范围为1981 - 2017年。使用我们的模型，我们将其结果复制到党派的特定演变上。其次，我们的模型提供了其他见解，例如随着时间的推移，局部内容的发展估计。第三，我们在主题层面上确定党派的关键短语。
Gentzkow, Shapiro and Taddy, Econometrica Vol 87, No 4, 2019 (henceforth GST) use a supervised text-based regression model to assess changes in partisanship in U.S. 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 1981-2017. 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.
