Coupling Couples With Copulas: Analysis of Assortative Matching on Risk Attitude

Nikoloulopoulos, Aristidis K.; Moffatt, Peter G.

doi:10.1111/ecin.12726

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…Copulas are elegantly captured in the Genest and MacKay (1986), Genest (1987) and also in De Oliveira (2016 and, among others. In the financial sector, a recent work by Nikoloulopoulos and Moffatt (2019) reminds us of the need to study dependence structures. There are also more general ambitions for the bivariate copula from a bigger perspective than we expect to show the aggregated effects in many other areas.…”

Section: Review Of Copula For Discrete Datamentioning

confidence: 99%

“…They applied their method on bivariate count time series data, where the marginals follow either a Poisson or zero-inflated Poisson distribution.Safari et al (2020) proposed a bivariate copula regression model to analyze cervical cancer data. They applied a bivariate copula to model and estimate joint distribution parameters Nikoloulopoulos and Moffatt (2019). used bivariate copulas to jointly model bivariate ordinal time-series responses with covariates for risks assessment of married couples.…”

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confidence: 99%

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Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

Fernando¹,

Alqawba²,

Samad³

et al. 2022

IJSP

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The class of bivariate integer-valued time series models, described via copula theory, is gaining popularity in the literature because of applications in health sciences, engineering, financial management and more. Each time series follows a Markov chain with the serial dependence captured using copula-based distribution functions from the Poisson and the zero-inflated Poisson margins. The copula theory is again used to capture the dependence between the two series. However, the efficiency and adaptability of the copula are being challenged because of the discrete nature of data and also in the case of zero-inflation of count time series. Likelihood-based inference is used to estimate the model parameters for simulated and real data with the bivariate integral of copula functions. While such copula functions offer great flexibility in capturing dependence, there remain challenges related to identifying the best copula type for a given application.  This paper presents a survey of the literature on bivariate copula for discrete data with an emphasis on the zero-inflated nature of the modelling. We demonstrate additional experiments on to confirm that the copula has potential as greater research area.

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Section: Review Of Copula For Discrete Datamentioning

confidence: 99%

mentioning

confidence: 99%

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

Fernando¹,

Alqawba²,

Samad³

et al. 2022

IJSP

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“…Following a similar framework of building bivariate models for ordinal panel data via [1], we constructed a class of bivariate integer-valued time series models using copula theory. Applying either the bivariate Gaussian copula or the bivariate t-copula functions, we jointly modeled two copula-based Markov time series models (see [2,3]).…”

Section: Introductionmentioning

confidence: 99%

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

2021

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A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models.

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“…Importantly, risk tolerance may cause individuals to both seek performance pay and marry those with similar risk preferences (Bacon, Conte, & Moffatt, 2014; Nikoloulopuolos & Moffatt, 2019). In this view risk tolerance is an important trait in everything from joint decisions on children and the nature of family investments to retirement decisions.…”

Section: Introductionmentioning

confidence: 99%

Performance pay and assortative matching

Bender

Green

Heywood

2019

Scottish J Political Eco

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We show that couples sort on performance pay with dual receipt couples much more likely than predicted by random. In addition, we show that the return to performance pay appears largely invariant to whether or not one's spouse earns performance pay. This implies that the distribution of family income will be less equitable than that of individual income as sorting concentrates the performance pay return among households with higher pay.

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Coupling Couples With Copulas: Analysis of Assortative Matching on Risk Attitude

Cited by 5 publications

References 56 publications

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

Performance pay and assortative matching

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