A GEOMETRIC APPROACH TO NONLINEAR ECONOMETRIC MODELS

• 成果类型: Article
• 署名作者: Andrews, Isaiah; Mikusheva, Anna
• 署名单位: Harvard University; Massachusetts Institute of Technology (MIT)
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12030
• 发表日期: 2016
• 页码: 1249-1264
• 关键词: inference; weak; identification; tests

摘要

Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced-form parameters is highly nonlinear. Such nonlinearity may arise for a variety of reasons, including weak identification. In this note, we begin by studying the problem of testing a curved null in a finite-sample Gaussian model. Using the curvature of the model, we develop new finite-sample bounds on the distribution of minimum-distance statistics. These bounds allow us to construct tests for composite hypotheses which are uniformly asymptotically valid over a large class of data generating processes and structural models.