Simultaneous inference for time-varying models

• Tác giả: Sayar Karmakar, Stefan Richter, Wei Biao Wu
• Ngôn ngữ: eng
• Ký hiệu phân loại: 511.8 Mathematical models (Mathematical simulation)
• Thông tin xuất bản: 2020
• Bộ sưu tập: Metadata
• ID: 165674

Tóm tắt

A general class of time-varying regression models is considered in this paper. We estimate the regression coefficients by using local linear M-estimation. For these estimators, weak Bahadur representations are obtained and are used to construct simultaneous confidence bands. For practical implementation, we propose a bootstrap based method to circumvent the slow logarithmic convergence of the theoretical simultaneous bands. Our results substantially generalize and unify the treatments for several time-varying regression and auto-regression models. The performance for ARCH and GARCH models is studied in simulations and a few real-life applications of our study are presented through analysis of some popular financial datasets.Comment: To appear at Journal of Econometrics