Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/119435
Authors: 
Breunig, Christoph
Year of Publication: 
2015
Series/Report no.: 
SFB 649 Discussion Paper 2015-016
Abstract: 
This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic's asymptotic distribu- tion under the MAR hypothesis is derived. We demonstrate that our results can be easily extended to a test of missing completely at random (MCAR) and miss- ing completely at random conditional on covariates X (MCAR(X)). A Monte Carlo study examines finite sample performance of our test statistic. An empirical illustration concerns pocket prescription drug spending with missing values; we reject MCAR but fail to reject MAR.
Subjects: 
incomplete data
missing-data mechanism
selection model
nonparametric hypothesis testing
consistent testing
instrumental variable
series estimation
JEL: 
C12
C14
Document Type: 
Working Paper

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