Missing (Completely?) At Random: Lessons from Insurance Studies
AbstractA dilemma frequently faced by empirical researchers is whether they should keep observations without complete information in the analysis. Assuming missingness is not biased in any perceivable direction, most studies use a complete case analysis approach, whereby only observations with complete information are kept for empirical estimation. However, the literature on statistics (e.g., Little and Rubin 2002) suggests that potential biases may arise from such practice, especially if missing data are not missing completely at random (MCAR). When there are missing data, Littles MCAR test (1988) can be performed to reveal whether imputation methods are necessary to minimize the problems arising from incomplete data. We take two recently studied insurance data sets as examples to show that missing data issues can be better handled.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by De Gruyter in its journal Asia-Pacific Journal of Risk and Insurance.
Volume (Year): 3 (2009)
Issue (Month): 2 (April)
Contact details of provider:
Web page: http://www.degruyter.com
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral 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: (Peter Golla).
If references are entirely missing, you can add them using this form.