Testing missing at random using instrumental variables

Christoph Breunig

No 2015-016, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: incomplete data; missing-data mechanism; selection model; nonparametric hypothesis testing; consistent testing; instrumental variable; series estimation (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2015-016

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