SpringerBriefs in Statistics

Cherubini, Umberto; Gobbi, Fabio; Mulinacci, Sabrina

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. 

Details

Published by: Springer

Publication Date: 2016-12-16

Format: Paperback

ISBN-13: 9783319480145

DOI: 10.1007/978-3-319-48015-2

Dimensions: 235cm x155cm

Pages: 90