Spectral density estimation and robust hypothesis testing using steep origin kernels without truncation

• 成果类型: Article
• 署名作者: Phillips, Peter C. B.; Sun, Yixiao; Jin, Sainan
• 署名单位: Yale University; University of California System; University of California San Diego; Peking University
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/j.1468-2354.2006.00398.x
• 发表日期: 2006
• 页码: 837-894
• 关键词: Heteroskedasticity; matrix

摘要

A new class of kernels for long-run variance and spectral density estimation is developed by exponentiating traditional quadratic kernels. Depending on whether the exponent parameter is allowed to grow with the sample size, we establish different asymptotic approximations to the sampling distribution of the proposed estimators. When the exponent is passed to infinity with the sample size, the new estimator is consistent and shown to be asymptotically normal. When the exponent is fixed, the new estimator is inconsistent and has a nonstandard limiting distribution. It is shown via Monte Carlo experiments that, when the chosen exponent is small in practical applications, the nonstandard limit theory provides better approximations to the finite sample distributions of the spectral density estimator and the associated test statistic in regression settings.