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Semiparametric Bayesian Instrumental Variables Estimation for Nonignorable Missing Instruments

Author

Listed:
  • Ryo Kato

    (Research Institute for Economics and Business Administration, Kobe University, JAPAN)

  • Takahiro Hoshino

    (Department of Economics, Keio University and RIKEN Center for Advanced Intelligence Project, JAPAN)

Abstract

This paper considers the case where instrumental variable (IV) are available to infer the effect of interested variable to the outcome (or the causal effect), but some components of IV are missing with the missing mechanism of not missing at random (NMAR). Although NMAR requires the analysis to prespecify the missing mechanism, it is unknown for us and what is worse, it is generally not identi ed. We use the IV distribution of original population as an auxiliary information, and show that missing mechanism can be represented as identi able nonparametric generalized additive model. We also introduce MCMC algorithm that impute the missing values and simultaneously estimate parameters of interested.

Suggested Citation

  • Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian Instrumental Variables Estimation for Nonignorable Missing Instruments," Discussion Paper Series DP2020-06, Research Institute for Economics & Business Administration, Kobe University.
    • Handle: RePEc:kob:dpaper:dp2020-06
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    File URL: https://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2020-06.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Instrumental variable; Missing not at random; Auxiliary information;
    All these keywords.

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