Robust regression in Stata
AbstractLeast-squares regression is a major workhorse in applied research. Yet its estimates may be deemed nonrobust under various conditions. One example is heavy-tailed error distributions, in which least-squares estimation may lose its cutting edge with respect to efficiency. More importantly, ordinary regression methods can produce biased results if the data are contaminated by a set of observations stemming from an alternative process. Various robust regression estimators have been proposed in the literature to address these problems, but they do not seem to be employed much in practical research. One reason for this underutilization may be a lack of convenient software implementations, as is exemplified by a close-to-complete absence of robust estimators from official Stata. In this talk, I will therefore present a number of user-written commands geared toward robust estimation of regression models.
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.
Bibliographic InfoPaper provided by Stata Users Group in its series German Stata Users' Group Meetings 2012 with number 05.
Date of creation: 04 Jun 2012
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-06-13 (All new papers)
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: (Christopher F Baum).
If references are entirely missing, you can add them using this form.