Convolution Copula Econometrics
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Beschreibung
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.
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.
Provides ideas for further research in the field of time series analysis and copula functions Presents an authoritative contribution on long memory features of macroeconomic and financial time series Explores the use of convolution-based econometric tools for forecasting Markov processes Features applications of the convolution-based technology such as tests of market efficiency
Autor*in
Umberto Cherubini
Themen in »Convolution Copula Econometrics«
62M05, 60G99 copula functions convolution-based process time series analysis stochastic processes long memory time series econometrics interest rates autoregressive process Markov process
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“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)
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