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2016 | nr 2 (89) | 246--285
Tytuł artykułu

Modelling Health Indicators in a Joint Framework via Factor Copula Models

Treść / Zawartość
Warianty tytułu
Modelowanie wskaźników zdrowotnych za pomocą kopuł czynnikowych
Języki publikacji
EN
Abstrakty
W ostatniej dekadzie mamy dostęp do szczegółowych danych zdrowotnych (np.: badanie SHARE). Modelowanie łącznego rozkładu wielu wskaźników zdrowotnych nie jest łatwym zadaniem. Literatura nie jest duża i nie adresuje właściwie np.: struktury zależności między wskaźnikami. Współwystępowanie chorób jest zaś kluczowe dla wydatków na opiekę zdrowotną. Celem artykułu jest pokazanie, że zależności te występują w stopniu, który nie może być ignorowany oraz rozszerzenie literatury o metody, które modelują łączny rozkład zdrowia elastycznie i wydajnie obliczeniowo. Są to dostępne od niedawna tzw. pair-copula constructions (PCC) (Aas et al., 2009). Mogą one być użyte przy wielu wymiarach zdrowia, gdzie inne metody są niekonkluzywne (Duclos i Echevin, 2012). Na podstawie danych z English Longitudinal Study of Ageing (ELSA) nt. statusu zdrowotnego, mobilności, wzroku, słuchu czy zdrowia emocjonalnego, estymujemy jednoczynnikowy model (Nikoloulopoulos i Joe, 2015). Wskaźniki zdrowia wykazują zależność w ogonach; kopule t(4) i t(5) wykazują najlepsze dopasowanie. Szczegółowe zależności są nie do wykrycia przez niedawno rozwinięte podejścia (Makdisi i Yazbeck, 2014).(abstrakt oryginalny)
EN
The problems of ageing societies in advanced economies have recently put emphasis on the evaluation of health of the elderly. Health is likely to determine job market activity of the increasing parts of the society. Appropriate modeling of health conditions is therefore key for policymaking, in particular given that detailed health data are now available via ageing surveys. Recently, there has been increased interest in modeling multiple ordinal health data. Makdisi and Yazbeck (2014) utilize the counting approach, which requires the transformation of multiple category health indicators into binary and lead to the loss of information but also changes the dependence structure. We offer a different approach which does not have these limitations and is feasible for highdimensional data (while, for example, stochastic dominance methods are inconclusive for many dimensions (Duclos and Echevin 2012). We use recently developed methods based on so-called vine pair-copula constructions (PCC) (Aas et al. 2009). We estimate a 1-factor copula model (Nikoloulopoulos and Joe 2015) for 24 health indicators taken from English Longitudinal Study of Ageing (ELSA) such as self-reported health status, mobility, eyesight, hearing and pain rating and questions related to emotional health. We show that there are substantial interdependencies in health data which cannot be neglected by dichotomization and aggregation, nor can they be detected by the standard multivariate probit model. t(4)- and t(5)- factor copula model provides the best fi t, and items that measure general optimism are most informative of the underlying factor. Groups are most heterogeneous along the employment status, with retired and disabled groups showing significantly more dependence than other groups in items related to mobility and general health status.(original abstract)
Rocznik
Numer
Strony
246--285
Opis fizyczny
Twórcy
  • Polish Academy of Sciences
  • Vistula University, Warsaw
Bibliografia
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  • Hua L., Joe H. (2014), Strength of tail dependence based on conditional tail expectation, "Journal of Multivariate Analysis", Vol. 123, pp. 143-159.
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  • Joe H. (2005), Asymptotic efficiency of the two-stage estimation method for copula- -based models, "Journal of Multivariate Analysis", Vol. 94, pp. 401-419.
  • Joe H. (2015), Dependence Modelling with Copulas, Taylor & Francis Group.
  • Krupskii P., Joe H. (2013), Factor copula models for multivariate data, "Journal of Multivariate Analysis", Vol. 120, pp. 85-101.
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  • Makdisi P., Yazbeck M. (2014), Measuring socioeconomic health inequalities in the presence of multiple categorical information, "Journal of Health Economics, Vol. 34, pp. 84-95.
  • Marmot M., Oldfi eld Z., Clemens S., Blake M., Phelps A., Nazroo J., Steptoe A., Rogers N., Banks J. (2016), English Longitudinal Study of Ageing, Waves 0-7, 1998-2015 (data collection), 24th Edition, UK Data Service, SN: 5050, http:// dx.doi.org/10.5255/UKDA-SN-5050-11
  • Nelsen R.B. (2006), An Introduction to Copulas, 2nd ed., Springer.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171437290

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