Statistical inference for conditional quantiles in nonlinear time series models文档.pdf

Journal of Econometrics ( ) – Contents lists available at ScienceDirect Journal of Econometrics journal homepage: /locate/jeconom Statistical inference for conditional quantiles in nonlinear time series models Mike K.P. So a,∗, Ray S.W. Chung b a Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong b Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong a r t i c l e i n f o a b s t r a c t Article history: This paper studies the statistical properties of a two-step conditional quantile estimator in nonlinear time Available online xxxx series models with unspecified error distribution. The asymptotic distribution of the quasi-maximum like- lihood estimators and the filtered empirical percentiles is derived. Three applications of the asymptotic Keywords: result are considered. First, we construct an interval estimator of the conditional quantile without any dis- Conditional quantile tributional assumptions. Second, we develop a specification test for the error distribution. Finally, using High-end quantile estimation Nonlinear time series the specification test, we propose methods for estimating the tail index of the error distribution that sup- Specification test ports the construction of a new estimator for the conditional quantile at the extreme tail. The asymptotic