Empirical Likelihood and Quantile Methods for Time Series

This book provides a comprehensive exploration of asymptotic theory in statistical inference for time series, focusing on innovative methods and robust applications.

This book, Empirical Likelihood and Quantile Methods for Time Series, delves into the essential principles of asymptotic theory related to statistical inference in time series analysis, particularly under nonstandard conditions such as infinite variance processes. It emphasizes not only the efficiency of the methods discussed but also their robustness and optimality, focusing on minimizing prediction errors. The integration of these concepts offers a comprehensive perspective on time series modeling, enhancing the reader's understanding of complex prediction challenges.

The work stands out as the first to apply generalized empirical likelihood to time series models in the frequency domain. It also introduces innovative estimation techniques motivated by minimizing quantile prediction errors, all without the necessity of a true model assumption. This fresh approach provides readers with valuable insights into hypothesis testing and the prediction problems that frequently arise in time series analysis. Furthermore, the nonparametric methods presented are adept at addressing economic and financial issues while avoiding overly stringent model restrictions.

By tackling the intricacies of infinite variance processes, Empirical Likelihood and Quantile Methods for Time Series enhances the accuracy of economic and financial data analysis. The methodologies discussed are versatile and applicable across a wide range of academic disciplines, offering advanced and unified developments in prediction forms, including multiple-point extrapolation and interpolation. Ultimately, this book equips readers with effective and robust estimation and testing techniques, enabling them to discern pivotal quantities in realistic time series models.