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Convolution Copula Econometrics : SpringerBriefs in Statistics - Umberto Cherubini

Convolution Copula Econometrics

By: Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci

eText | 1 December 2016

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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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Non-FictionEconomicsEconometricsEconomic StatisticsMathematicsProbability & StatisticsApplied MathematicsStochastics

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