This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.
Publisher: Springer International Publishing AG
Number of pages: 90
Weight: 226 g
Dimensions: 235 x 155 x 5 mm
Edition: 1st ed. 2016
"The goal of the book is to gather the main concepts of copula function theory that can be applied to the analysis of time series (so-called convolution-based copulas), and some new ideas, linked to copulas, such as estimation of copula-based Markov processes. ... The book will be useful for the researchers working in econometrics, interest rate, Markov processes and copulas fields." (Anatoliy Swishchuk, zbMATH 1360.62006, 2017)