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Testing Missing at Random Using Instrumental Variables

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  • Christoph Breunig

Abstract

This article 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 distribution under the MAR hypothesis is derived. In particular, our results can be applied to testing missing completely at random (MCAR). A Monte Carlo study examines finite sample performance of our test statistic. An empirical illustration analyzes the nonresponse mechanism in labor income questions.

Suggested Citation

  • Christoph Breunig, 2019. "Testing Missing at Random Using Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 223-234, April.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:2:p:223-234
    DOI: 10.1080/07350015.2017.1302879
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    Cited by:

    1. Hairu Wang & Zhiping Lu & Yukun Liu, 2023. "Score test for missing at random or not under logistic missingness models," Biometrics, The International Biometric Society, vol. 79(2), pages 1268-1279, June.
    2. Ying Fang & Ming Lin & Shengfang Tang & Zongwu Cai, 2021. "Testing Conditional Independence in Macroeconomic Policy Evaluation for Time Series Data," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202118, University of Kansas, Department of Economics, revised Sep 2021.
    3. Chassan, Malika & Concordet, Didier, 2023. "How to test the missing data mechanism in a hidden Markov model," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    4. Breunig, Christoph & Haan, Peter, 2021. "Nonparametric regression with selectively missing covariates," Journal of Econometrics, Elsevier, vol. 223(1), pages 28-52.
    5. Rui Duan & C. Jason Liang & Pamela Shaw & Cheng Yong Tang & Yong Chen, 2020. "Missing at Random or Not: A Semiparametric Testing Approach," Papers 2003.11181, arXiv.org.
    6. Fang, Ying & Tang, Shengfang & Cai, Zongwu & Lin, Ming, 2020. "An alternative test for conditional unconfoundedness using auxiliary variables," Economics Letters, Elsevier, vol. 194(C).
    7. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    8. Wei Luo, 2022. "On efficient dimension reduction with respect to the interaction between two response variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 269-294, April.

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