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Testing Conditional Mean Independence Under Symmetry

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  • Tao Chen
  • Yuanyuan Ji
  • Yahong Zhou
  • Pingfang Zhu

Abstract

Conditional mean independence (CMI) is one of the most widely used assumptions in the treatment effect literature to achieve model identification. We propose a Kolmogorov–Smirnov-type statistic to test CMI under a specific symmetry condition. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test. Results from a simulation study suggest that our test can work very well even in small to moderately sized samples. As an empirical illustration, we apply our test to a dataset that has been used in the literature to estimate the return on college education in China, to check whether the assumption of CMI is supported by the dataset and show the plausibility of the extra symmetry condition that is necessary for this new test.

Suggested Citation

  • Tao Chen & Yuanyuan Ji & Yahong Zhou & Pingfang Zhu, 2018. "Testing Conditional Mean Independence Under Symmetry," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 615-627, October.
    • Handle: RePEc:taf:jnlbes:v:36:y:2018:i:4:p:615-627
    DOI: 10.1080/07350015.2016.1219263
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    Cited by:

    1. Slichter, David, 2020. "Smile: A Simple Diagnostic for Selection on Observables," MPRA Paper 99921, University Library of Munich, Germany.
    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.

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