Estimation and Inference for Linear Models with Two-Way Fixed Effects and Sparsely Matched Data

• 成果类型: Article
• 署名作者: Verdier, Valentin
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00807
• 发表日期: 2020-03
• 页码: 1-16
• 关键词: high wage workers; panel-data; dynamic-models; high-school; teacher; specification; Heterogeneity; impacts; tests; FIRMS

摘要

Models with multiway fixed effects are frequently used to address selection on unobservables. The data used for estimating these models often contain few observations per value of either indexing variable (sparsely matched data). I show that this sparsity has important implications for inference and propose an asymptotically valid inference method based on subsetting. Sparsity also has important implications for point estimation when covariates or instrumental variables are sequentially exogenous (e.g., dynamic models), and I propose a new estimator for these models. Finally, I illustrate these methods by providing estimates of the effect of class size reductions on student achievement.