Newest papers

Which Text Matters: Handling Model Uncertainty in Text-as-Data Regressions, with an Application to Central Bank Communication

Martin Feldkircher, Bernd Prostmaier, Paul Hofmarcher, Pierre Siklos · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.19720 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

社会科学家经常将文本转换为主题份额、理想点或情绪分数等变异,并将其插入感兴趣的统计模型。这两个步骤的设计方便,但很少由理论引导:很少告诉分析师哪些基于文本的变异要构建或包括。每个方法体现了可争议的测量选择,并且同一构建的竞争式操作化可以支持不同的结论,所以一个单一的研究问题产生了竞争的回归模型菜单。哪个文本构建的变异属于一个模型,以及在哪个功能上,因此是一个模型不确定性的问题。我们提议同一构建的尖和低的通用增量模型(SSGAMs),这些模型可以共同选择变异,并确定其效果是否是线性或非线性。我们用强

Machine translation (Meta M2M100); the English original follows.

Abstract

Social scientists routinely convert text into covariates like topic shares, ideal points or sentiment scores, and insert them into statistical models of interest. This two-step design is convenient, but it is rarely guided by theory: little tells the analyst which text-based covariates to construct or include. Each approach embodies contestable measurement choices, and competing operationalizations of the same construct can support different conclusions, so a single research question gives rise to a menu of competing regression models. Which text constructed covariates belong in a model, and in what functional form, is thus a problem of model uncertainty. We propose spike-and-slab generalized additive models (SSGAMs), which jointly select covariates and determine whether their effects are linear or nonlinear. We illustrate the approach with the communication of a powerful unelected institution, asking which of 45 competing text indicators from 224 monthly European Central Bank press conferences, spanning late 2002 to mid-2025, track professional inflation expectations. A sparse set is selected; widely used alternatives, and every tested nonlinear effect, carry no incremental information.

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Topics: Covariate, Construct (python library), Computer science, Set (abstract data type), Econometrics, Ideal (ethics)

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