A CONDITIONAL-HETEROSKEDASTICITY-ROBUST CONFIDENCE INTERVAL FOR THE AUTOREGRESSIVE PARAMETER

• 成果类型: Article
• 署名作者: Andrews, Donald W. K.; Guggenberger, Patrik
• 署名单位: Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00369
• 发表日期: 2014-05
• 页码: 376-381
• 关键词: median-unbiased estimation; unit-root tests; limit theory; ar(1) model; bootstrap; inference

摘要

This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in finite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity.