Copula Modeling: An Introduction for Practitioners

By Pravin K. Trivedi, David M. Zimmer. 2007

965 citations recorded by the source corpus.

This article explores the copula approach for econometric modeling of joint parametric distributions. Although theoretical foundations of copulas are complex, this paper demonstrates that practical implementation and estimation are relatively straightforward. An attractive feature of parametrically specified copulas is that estimation and inference are based on standard maximum likelihood procedures, and thus copulas can be estimated using desktop econometric software. This represents a substantial advantage of copulas over recently proposed simulation-based approaches to joint modeling.

Subfields: Demography, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7543c30aee33eea5fe9a9086be7e1a1bfc09951c

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Citations in the corpus

Copula-Based Maximum-Likelihood Estimation of Sample-Selection Models (2013, background): “Also see Nelsen (2006) for a general introduction to copulas and Trivedi and Zimmer (2005) for applications of copulas in other econometric models.”
Pairs trading: A copula approach (2013, background): “(Trivedi and Zimmer, 2007) Hence, the use of copula is able to capture the co-movement between stocks accurately enough to identify trading signals, which standard linear correlation analysis is not robust enough to accomplish (Ferreira, 2008).”
A joint model for mode choice and escort decisions of school trips (2015, methodology): “𝐵𝐼𝐶 = −2 × ln(L) + k′ × ln(n) (15) As per this equation, the lower the BIC statistic is, the better the model fits the data (Trivedi and Zimmer 2007).”
Correlation detection strategies in microbial data sets vary widely in sensitivity and precision (2016, methodology): “Supplementary Table 1 and the Methods section detail how mock data were generated, and all code, test-code and documentation is available at ftp.microbio.me/pub/cooccurrence_files.zip.”
The Gender Gap between Earnings Distributions (2019, methodology): “This copula is widely used in empirical work (Meester and MacKay 1994; Trivedi and Zimmer 2005).”
Copula Theory: An Introduction (2010, background): “Other surveys concerning copulas (from various perspectives) can be found as well in the literature; just to make few same examples, we refer the interested reader to [30, 56, 73, 76, 93, 117, 158, 181, 189, 193, 195].”
Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis (2016, background): “The multivariate tobit model 124 (Huang et al., 1987; Huang, 1999; Trivedi and Zimmer, 2005) accounts for contemporaneous (cross-125 equation) error correlation among the accident injury-severity rates (i.e., property damage only crashes per 126 100-million vehicle miles traveled, injury crashes…”
Copula-Based Approach to Accommodate Residential Self-Selection Effects in Travel Behavior Modeling (2009, methodology): “The most widely used approach to select among the competing non-nested copula models is the Bayesian Information Criterion (or BIC; see Quinn, 2007, Genius and Strazzera, 2008, Trivedi and Zimmer, 2007, page 65).”