Publication Type

Journal Article

Publication Date

1-2017

Abstract

When analyzing data with missing data, a commonly used method is the inverse probability weighting (IPW) method, which reweights estimating equations with propensity scores. The popularity of the IPW method is due to its simplicity. However, it is often being criticized for being inefficient because most of the information from the incomplete observations is not used. Alternatively, the regression method is known to be efficient but is nonrobust to the misspecification of the regression function. In this article, we propose a novel way of optimally combining the propensity score function and the regression model. The resulting estimating equation enjoys the properties of robustness against misspecification of either the propensity score or the regression function, as well as being locally semiparametric efficient. We demonstrate analytically situations where our method leads to a more efficient estimator than some of its competitors. In a simulation study, we show the new method compares favorably with its competitors in finite samples. Supplementary materials for this article are available online.

Keywords

Inverse probability weighting, Missing data, Regression estimate, Semiparametric efficiency

Discipline

Economics

Research Areas

Macroeconomics

Publication

Journal of Business and Economic Statistics

Volume

35

Issue

1

First Page

86

Last Page

97

ISSN

0735-0015

Publisher

Taylor & Francis: STM, Behavioural Science and Public Health Titles

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Included in

Economics Commons

Share

COinS