Simple robust testing of hypotheses in nonlinear models

• 成果类型: Article
• 署名作者: Bunzel, H; Kiefer, NM; Vogelsang, TJ
• 署名单位: Iowa State University; Cornell University; Cornell University; Aarhus University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214501753209068
• 发表日期: 2001
• 页码: 1088-1096
• 关键词: Time-series regression; heteroskedasticity

摘要

We develop test statistics to test hypotheses in nonlinear weighted regression models with serial correlation or conditional heteroscedasticity of unknown form. The novel aspect is that these tests are simple and do not require the use of heteroseedasticity autocorrelation-consistent (HAC) covariance matrix estimators. Th-is new class of tests uses stochastic transformations to eliminate nuisance parameters as a substitute for consistently estimating the nuisance parameters. We derive the limiting null distributions of these new tests in a general nonlinear setting, and show that although the tests have nonstandard distributions, the distributions depend only on the number of restrictions being tested. We perform some simulations on a simple model and apply the new method of testing to an empirical example and illustrate that the size of the new test is less distorted than tests using HAC covariance matrix estimators.