Bias and inefficiency of an ordinary least squares estimator for logit regressions with continuous dependent variables measured with error

Sunil Sapra ()

Applied Economics Letters, 1998, vol. 5, issue 12, 745-746

Abstract: In a recent paper in this journal, Manning showed that the ordinary least squares (OLS) estimator of regression coefficients in a logit model with continuous and bounded dependent variable measured with error is unbiased but inefficient. This note shows that, contrary to the author's claim, the OLS estimator is biased in general. Manning's form of heteroscedasticity is also shown to be incorrect. It is shown that Manning's assumptions on the distribution of measurement error are inadequate for determining the form of heteroscedasticity.

Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.informaworld.com/openurl?genre=article& ... 40C6AD35DC6213A474B5 (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:apeclt:v:5:y:1998:i:12:p:745-746

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/135048598353925

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().