Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models

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Tác giả: Gregory Cox, Xiaoxia Shi

Ngôn ngữ: eng

Ký hiệu phân loại: 512.88 Algebra

Thông tin xuất bản: 2019

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 163096

We propose a simple test for moment inequalities that has exact size in normal models with known variance and has uniformly asymptotically exact size more generally. The test compares the quasi-likelihood ratio statistic to a chi-squared critical value, where the degree of freedom is the rank of the inequalities that are active in finite samples. The test requires no simulation and thus is computationally fast and especially suitable for constructing confidence sets for parameters by test inversion. It uses no tuning parameter for moment selection and yet still adapts to the slackness of the moment inequalities. Furthermore, we show how the test can be easily adapted for inference on subvectors for the common empirical setting of conditional moment inequalities with nuisance parameters entering linearly.
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