Measuring the Impact of Nonignorable Non-Response in Panel Data with Non-Monotone Missingness

Journal of Applied Econometrics, Forthcoming

53 Pages Posted: 10 Dec 2009

Date Written: September 20, 2009


The analysis of panel data with non-monotone nonresponse often relies on the critical and untestable assumption of ignorable missingness. It is important to assess the consequences of departures from the ignorability assumption. Non-monotone nonresponse, however, can often make such sensitivity analysis infeasible because the likelihood functions for alternative models involve high-dimensional and difficult-to-evaluate integrals with respect to missing outcomes. We develop an extension of the local sensitivity method that overcomes computational difficulty and completely avoids fitting alternative models and evaluating these high-dimensional integrals. The proposed method is applicable to a wide range of panel outcomes. We apply the method to a Smoking Trend dataset where we relax the standard ignorability assumption and evaluate how smoking trend estimates in different groups of U.S. young adults are affected by alternative assumptions about the missing-data mechanism. The main finding is that the standard estimate in the black-male group is sensitive to nonignorable missingness but those in other groups are reasonably robust.

Keywords: Generalized Least Square,Generalized Linear Mixed Model, Nonignorability, Sensitivity Analysis

JEL Classification: C01, C23, C33

Suggested Citation

Qian, Yi and Xie, Hui, Measuring the Impact of Nonignorable Non-Response in Panel Data with Non-Monotone Missingness (September 20, 2009). Journal of Applied Econometrics, Forthcoming. Available at SSRN:

Yi Qian (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Hui Xie

University of Illinois ( email )

1200 W Harrison St
Chicago, IL 60607
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics