The Economics Seminar Series presents Bhavna Rai discussing "Imputing missing covariate values in nonlinear models" on Friday, Oct. 23, from 3 to 4:15 p.m.

Rai is a graduate student  in the Department of Economics at Michigan State University.

For more information about the Economics Seminars, contact Dr. Roberto Duncan .

Abstract: I study the problem of missing covariate values in nonlinear models with continuous or discrete covariates. In order to use the information in the incomplete cases, I propose an inverse probability weighted one-step imputation estimator that provides gains in efficiency relative to the complete cases estimator using a reduced form for the outcome in terms of the always-observed covariates. Unlike the two-step imputation and dummy variable methods commonly used in empirical work, my estimator is consistent for a wide class of nonlinear models. It relies only on the commonly used "missing at random" assumption, and provides a specification test for the resulting restrictions. I show how the results apply to nonlinear models for fractional and nonnegative responses.