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Indirect Inference with Endogenously Missing Exogenous Variables

Author

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  • Saraswata Chaudhuriy
  • David T. Frazierz
  • Eric Renault

Abstract

We consider consistent estimation of parameters in a structural model by Indirect Inference (II) when the exogenous variables can be missing at random (MAR) endogenously. We demonstrate that II procedures which simply discard sample units with missing observations can yield inconsistent estimates of the true structural parameters. By inverse probability weighting (IPW) the “complete case” observations, i.e., sample units with no missing variables for the observed and simulated samples, we propose a new method of II to consistently estimate the structural parameters of interest. Asymptotic properties of the new estimator are discussed. An illustration is provided based on a multinomial probit model. A small scale Monte-Carlo study in this model demonstrates the severe bias incurred by existing II estimators, and its subsequent correction by our new II estimator.

Suggested Citation

  • Saraswata Chaudhuriy & David T. Frazierz & Eric Renault, 2016. "Indirect Inference with Endogenously Missing Exogenous Variables," CIRANO Working Papers 2016s-15, CIRANO.
    • Handle: RePEc:cir:cirwor:2016s-15
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    References listed on IDEAS

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    JEL classification:

    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • L62 - Industrial Organization - - Industry Studies: Manufacturing - - - Automobiles; Other Transportation Equipment; Related Parts and Equipment
    • F23 - International Economics - - International Factor Movements and International Business - - - Multinational Firms; International Business
    • F61 - International Economics - - Economic Impacts of Globalization - - - Microeconomic Impacts

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