Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model

Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model Previous work (Bell and Jones, Demogr Res 2013a; Bell and Jones, Soc Sci Med 2013c; Luo and Hodges, Under review 2013) has shown that, when there are trends in either the period or cohort residuals of Yang and Land’s Hierarchical age–period–cohort (APC) model (Yang and Land, Sociol Methodol 36:75–97 2006; Yang and Land, APC analysis: new models, methods, and empirical applications. CRC Press, Boca Raton 2013), the model can incorrectly estimate those trends, because of the well-known APC identification problem. Here we consider modelling possibilities when the age effect is known, allowing any period or cohort trends to be estimated. In particular, we suggest the application of informative priors, in a Bayesian framework, to the age trend, and we use a variety of simulated but realistic datasets to explicate this. Similarly, an informative prior could be applied to an estimated period or cohort trend, allowing the other two APC trends to be estimated. We show that a very strong informative prior is required for this purpose. As such, models of this kind can be fitted but are only useful when very strong evidence of the age trend (for example physiological evidence regarding health) is available. Alternatively, a variety of strong priors can be tested and the most plausible solution argued for on the basis of theory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model

Loading next page...
 
/lp/springer_journal/bayesian-informative-priors-with-yang-and-land-s-hierarchical-age-tpePn1bLpp
Publisher
Springer Netherlands
Copyright
Copyright © 2013 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-013-9985-3
Publisher site
See Article on Publisher Site

Abstract

Previous work (Bell and Jones, Demogr Res 2013a; Bell and Jones, Soc Sci Med 2013c; Luo and Hodges, Under review 2013) has shown that, when there are trends in either the period or cohort residuals of Yang and Land’s Hierarchical age–period–cohort (APC) model (Yang and Land, Sociol Methodol 36:75–97 2006; Yang and Land, APC analysis: new models, methods, and empirical applications. CRC Press, Boca Raton 2013), the model can incorrectly estimate those trends, because of the well-known APC identification problem. Here we consider modelling possibilities when the age effect is known, allowing any period or cohort trends to be estimated. In particular, we suggest the application of informative priors, in a Bayesian framework, to the age trend, and we use a variety of simulated but realistic datasets to explicate this. Similarly, an informative prior could be applied to an estimated period or cohort trend, allowing the other two APC trends to be estimated. We show that a very strong informative prior is required for this purpose. As such, models of this kind can be fitted but are only useful when very strong evidence of the age trend (for example physiological evidence regarding health) is available. Alternatively, a variety of strong priors can be tested and the most plausible solution argued for on the basis of theory.

Journal

Quality & QuantitySpringer Journals

Published: Dec 25, 2013

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off