Convolution Copula Econometrics by Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci

By Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci

This ebook offers a unique method of time sequence econometrics, which reports the habit of nonlinear stochastic tactics. This method permits an arbitrary dependence constitution within the increments and offers a generalization with admire to the normal linear self sufficient increments assumption of classical time sequence types. The e-book deals an answer to the matter of a basic semiparametric method, that's given via an idea known as C-convolution (convolution of established variables), and the corresponding conception of convolution-based copulas. meant for econometrics and statistics students with a different curiosity in time sequence research and copula capabilities (or different nonparametric approaches), the publication can also be priceless for doctoral scholars with a uncomplicated wisdom of copula features desirous to find out about the most recent examine advancements within the box.

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Extra resources for Convolution Copula Econometrics

Sample text

We notice the asymmetric dependence structure of the time series which becomes stronger as τ increases. 1 Semiparametric Estimation This section introduces semiparametric estimators of a copula-based Markov model and establishes their asymptotic properties under easily verifiable condition. 2 Copula-Based Markov Processes: Estimation, Mixing Properties … 51 (a) 5 0 −5 0 100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000 (b) 5 0 −5 0 (c) 5 0 −5 0 Fig.

We conclude this section with the two main Theorems concerning consistency and asymptotic normality. 1 (Chen et al. 2 in Chen et al. 2009), if K n → ∞ and Knn → 0 we have γˆ n − γ = o p (1). Let ρ : ρ(γ). 2 (Chen et al. 7 in Chen et al. 2009), if X t is strictly stationary β-mixing we have n(ρ(γˆ n ) − ∗ ) 2 ). 2 Copula-Based Markov Processes: Estimation, Mixing Properties … 55 weak dependence in the time series considered. Among other results, Beare (2010), Chen et al. (2009), and Ibragimov and Lentzas (2009) provide a study of persistence properties of of stationary copula-based Markov processes.

1 (Chen et al. 2 in Chen et al. 2009), if K n → ∞ and Knn → 0 we have γˆ n − γ = o p (1). Let ρ : ρ(γ). 2 (Chen et al. 7 in Chen et al. 2009), if X t is strictly stationary β-mixing we have n(ρ(γˆ n ) − ∗ ) 2 ). 2 Copula-Based Markov Processes: Estimation, Mixing Properties … 55 weak dependence in the time series considered. Among other results, Beare (2010), Chen et al. (2009), and Ibragimov and Lentzas (2009) provide a study of persistence properties of of stationary copula-based Markov processes.