Alternative methods for estimating systems of (health) equations
This paper considers the simultaneous explanation of mortality risk, health and lifestyles, using a reduced-form system of equations in which the multivariate distribution is defined by the copula. A copula approximation of the joint distribution allows one to avoid usually implicit distributional assumptions, allowing potentially more robust and efficient estimates to be retrieved. By applying the theory of inference functions the parameters of each lifestyle, health and mortality equation can be estimated separately to the parameters of association found in their joint distribution, simplifying analysis considerably. The use of copulas also enables estimation of skewed multivariate distributions for the latent variables in a multivariate model of discrete response variables. This flexibility provides more precise estimates with more appropriate distributional assumptions, but presents explicit trade-offs during analysis. Information that can be retrieved concerning distributional assumptions, skewness and tail dependence require prioritisation such that different needs could generate a different ’best’ model even for the same data.
|Date of creation:||Jul 2006|
|Date of revision:|
|Contact details of provider:|| Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom|
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/postgrad/herc/hedg/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:yor:hectdg:06/05. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jane Rawlings)
If references are entirely missing, you can add them using this form.