Likelihood estimation and inference in a class of nonregular econometric models

• 成果类型: Article
• 署名作者: Chernozhukov, V; Hong, A
• 署名单位: Massachusetts Institute of Technology (MIT); Duke University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2004.00540.x
• 发表日期: 2004
• 页码: 1445-1480
• 关键词: empirical-models; structural models

摘要

We study inference in structural models with a jump in the conditional density, where location and size of the jump are described by regression curves. Two prominent examples are auction models, where the bid density jumps from zero to a positive value at the lowest cost, and equilibrium job-search models, where the wage density jumps from one positive level to another at the reservation wage. General inference in such models remained a long-standing, unresolved problem, primarily due to nonregularities and computational difficulties caused by discontinuous likelihood functions. This paper develops likelihood-based estimation and inference methods for these models, focusing on optimal (Bayes) and maximum likelihood procedures. We derive convergence rates and distribution theory, and develop Bayes and Wald inference. We show that Bayes estimators and confidence intervals are attractive both theoretically and computation ally, and that Bayes confidence intervals, based on posterior quantiles, provide a valid large sample inference method.