Deconvolution from two order statistics

• 成果类型: Article
• 署名作者: Cho, JoonHwan; Luo, Yao; Xiao, Ruli
• 署名单位: State University of New York (SUNY) System; Binghamton University, SUNY; University of Toronto; Indiana University System; Indiana University Bloomington
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2077
• 发表日期: 2024
• 页码: 1065-1106
• 关键词: Measurement error; order statistics; nonparametric identification; spacing; cross-sum; C14; C23; C57

摘要

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The identification result confirms a hypothesis by Athey and Haile (2002) for a symmetric ascending auction model with unobserved heterogeneity. Extensions allow for heterogeneous measurement errors, broadening the applicability to additional empirical settings, including asymmetric auctions and wage offer models. We adapt an existing simulated sieve estimator and illustrate its performance in finite samples.