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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.
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