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Convolution Copula Econometrics

  • Umberto Cherubini
  • Fabio Gobbi
  • Sabrina Mulinacci
Book

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-x
  2. Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci
    Pages 1-17
  3. Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci
    Pages 19-42
  4. Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci
    Pages 43-59
  5. Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci
    Pages 61-78
  6. Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci
    Pages 79-90

About this book

Introduction

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.

Keywords

62M05, 60G99 copula functions convolution-based process time series analysis stochastic processes long memory time series econometrics interest rates autoregressive process Markov process

Authors and affiliations

  • Umberto Cherubini
    • 1
  • Fabio Gobbi
    • 2
  • Sabrina Mulinacci
    • 3
  1. 1.University of BolognaBolognaItaly
  2. 2.University of BolognaBolognaItaly
  3. 3.University of BolognaBolognaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-48015-2
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-48014-5
  • Online ISBN 978-3-319-48015-2
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site