Partially Adaptive Estimation of the Censored Regression Model

Randall Lewis and James McDonald

Econometric Reviews, 2014, vol. 33, issue 7, 732-750

Abstract: Data censoring causes ordinary least squares estimates of linear models to be biased and inconsistent. Tobit, semiparametric, and partially adaptive estimators have been considered as possible solutions. This paper proposes several new partially adaptive estimators that cover a wide range of distributional characteristics. A simulation study is used to investigate the estimators' relative efficiency in these settings. The partially adaptive censored regression estimators have little efficiency loss for censored normal errors and may outperform Tobit and semiparametric estimators considered for non-normal distributions. An empirical example of out-of-pocket expenditures for a health insurance plan provides an example, which supports these results.

Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2012.690691 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:33:y:2014:i:7:p:732-750

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2012.690691

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().