Testing missing at random using instrumental variables

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SFB 649 Discussion Paper ; No. 2015-016

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Hypothesis Testing: General ; Semiparametric and Nonparametric Methods: General
• Thema: incomplete data ; missing-data mechanism ; selection model ; nonparametric hypothesis testing ; consistent testing ; instrumental variable ; series estimation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Breunig, Christoph
• Ereignis: Veröffentlichung
• (wer): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (wo): Berlin
• (wann): 2015

Weitere Informationen

• Handle: http://hdl.handle.net/10419/119435
• Letzte Aktualisierung: 10.03.2025, 11:46 MEZ