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Partial identification of nonlinear peer effects models with missing data

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  • Carlos Madeira

    (Central Bank of Chile)

Abstract

This paper examines inference on social interactions models in the presence of missing data on outcomes. In these models, missing data on outcomes imply an incomplete data problem on both the endogenous variable and the regressors. However, getting a sharp estimate of the partially identified coefficients is computationally difficult. Using a monotonicity property of the peer effects and a mean independence condition of individual decisions on the missing data, I show partial identification results for the binary choice peer effect model. A Monte Carlo exercise then summarizes the computational time and the accuracy performance of the interval estimators under some calibrations.

Suggested Citation

  • Carlos Madeira, 2022. "Partial identification of nonlinear peer effects models with missing data," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-18, December.
    • Handle: RePEc:spr:sjecst:v:158:y:2022:i:1:d:10.1186_s41937-022-00093-5
    DOI: 10.1186/s41937-022-00093-5
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    References listed on IDEAS

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    More about this item

    Keywords

    Social interactions; Binary choice; Partial identification;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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