Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions

• 成果类型: Article
• 署名作者: Spady, Richard H.; Stouli, Sami
• 署名单位: Johns Hopkins University; University of Oxford; University of Bristol; University of Melbourne
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA19153
• 发表日期: 2025
• 页码: 1885-1913
• 关键词: CONDITIONAL DISTRIBUTION; adaptive lasso; quantile; likelihood; identification; parameters; monotone; CURVES

摘要

We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible maximum likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework.