Christopher R. Bollinger

Bounding mean regressions when a binary regressor is mismeasured

In this paper I examine identification and estimation of mean regression models when a binary regressor is mismeasured. I prove that bounds for the model parameters are identified and provide simple estimators which are consistent and asymptotically normal. When stronger prior information about the probability of misclassification is available, the bounds can be made tighter. Again, a simple estimator for these cases is provided. All results apply to parametric and nonparametric models. The paper concludes with a short empirical example.

Modeling Discrete Choice with Response Error: Food Stamp Participation

Abstract Validation of Food Stamp program participation by Marquis and Moore revealed net bias of 13% in mean estimates of participation in the 1984 Survey of Income and Program Participation. We extend the analysis, incorporating demographic and economic covariates in models for underreporting and overreporting. We use the resulting models of response error to refine estimates of participation models using pseudo-maximum likelihood estimation methods. We find that the probability of underreporting rises with increased family income. Estimates of the participation model indicate the presence of substantial bias in income- and asset-related parameters compared to values obtained without incorporating response error.