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EnglishThis 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 |
| Autori: | |
| Anno: | 2016 |
| Serie: | |
| Citazione: | Cherubini, U., Gobbi, F., & Mulinacci, S. (2016). Convolution Copula Econometrics. Cham : Springer. |
| Handle: | http://hdl.handle.net/11365/1118180 |
| ISBN: | 978-3-319-48014-5 978-3-319-48015-2 |
| Appare nelle tipologie: | 3.1 Monografia o trattato scientifico |
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