Regression Coefficient Identification Decay in the Presence of Infrequent Classification Errors
Recent evidence from Bound et al. (2001) and Black et al. (2003) suggests that reporting errors in survey data routinely violate all of the classical measurement error assumptions. The econometrics literature has not considered the consequences of arbitrary measurement error for identification of regression coefficients. This paper highlights the severity of the identification problem given the presence of even infrequent arbitrary errors in a binary regressor. In the empirical component, health insurance misclassification rates of less than 1.3 percent generate double-digit percentage point ranges of uncertainty about the variable's true marginal effect on the use of health services.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||11 Jun 2007|
|Date of revision:|
|Publication status:||Published in Review of Economics and Statistics, November 2010, vol. 92 no. 4, pp. 1017-1023|
|Contact details of provider:|| Postal: |
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:isu:genres:12822. See general 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: (Stephanie Bridges)The email address of this maintainer does not seem to be valid anymore. Please ask Stephanie Bridges to update the entry or send us the correct address
If references are entirely missing, you can add them using this form.