Covariate Measurement Error in Quadratic Regression
We consider quadratic regression models where the explanatory variable is measured with error. The effect of classical measurement error is to flatten the curvature of the estimated function. The effect on the observed turning point depends on the location of the true turning point relative to the population mean of the true predictor. Two methods for adjusting parameter estimates for the measurement error are compared. First, two versions of regression calibration estimation are considered. The second approach uses moment-based methods which require no assumptions about the distribution of the covariates measured with error.
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.
When requesting a correction, please mention this item's handle: RePEc:nuf:econwp:1999-w2. 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: (Maxine Collett)
If references are entirely missing, you can add them using this form.