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.
Cherubini, U., Gobbi, F., & Mulinacci, S. (2016). Convolution Copula Econometrics. Cham : Springer.
|Titolo:||Convolution Copula Econometrics|
|Citazione:||Cherubini, U., Gobbi, F., & Mulinacci, S. (2016). Convolution Copula Econometrics. Cham : Springer.|
|Appare nelle tipologie:||3.1 Monografia o trattato scientifico|
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