COPULAS AND TEMPORAL DEPENDENCE

By Brendan K. Beare. 2008

152 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/048c92a4d503eb422cdfe1087a26856b01b0c0d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Andrew J. Patton
2. Timothy M. Christensen
3. Wei Biao Wu
4. Yanping Yi
5. Yanqin Fan
6. Xiaohong Chen
7. Demian Pouzo
8. M. D. Cattaneo

In Finance

1. Andrew J. Patton
2. Yanqin Fan
3. Wei Biao Wu
4. Xiaohong Chen
5. Timothy M. Christensen
6. Yanping Yi
7. Brendan K. Beare
8. Claudia Czado

In Statistics and Probability

1. Andrew J. Patton
2. Timothy M. Christensen
3. Wei Biao Wu
4. Yanping Yi
5. Yanqin Fan
6. Xiaohong Chen
7. Brendan K. Beare
8. Andrés Santos

Citations in the corpus

• A review of copula models for economic time series (2012, background): “Other work on the dependence properties of copula-based Markov processes includes Beare [10], Beare [11], Beare and Seo [12], Bouyé and Salmon [20], Chen et al.”
• Copula Methods for Forecasting Multivariate Time Series (2013, methodology): “This chapter will focus exclusively on multivariate forecasting problems using copula-based models, and exclude univariate copula-based models, such as those considered by Darsow, et al. (1992), Ibragimov (2009), Beare (2010), Chen and Fan (2006a) and Chen, et al. (2009) for example.”
• Copula-Based Nonlinear Quantile Autoregression (2009, unknown): “See Beare (2008) for temporal dependence properties of copula-based strictly stationary Markov processes.”
• Copulas in Econometrics (2014, background): “The majority of applications of copula-based multivariate models are in time series, so we make such models the focus of this section (for a discussion of copula-based univariate models, see, e.g., Darsow et al. 1992, Chen & Fan 2006b, Chen et al. 2009, Ibragimov 2009, Beare 2010).”
• Nonparametric stochastic discount factor decomposition (2014, background): “beta-mixing or rho-mixing because many popular models for macroeconomic or nancial time series imply this form of weak dependence. Examples include copula-based Markov models (Chen, Wu, and Yi, 2009; Beare, 2010) and discretely sampled Markov diusion processes (Chen, Hansen, and Carrasco, 2010). The”
• Efficient Estimation of Copula-Based Semiparametric Markov Models (2009, methodology): “…βmixing temporal dependence measure is purely determined by the properties of the copulas (and does not depend on the invariant distributions); and Beare (2008) provides simple sufficient conditions for geometric β-mixing in terms of copulas without any tail dependence [such as Gaussian, Frank…”