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Binary and Ordered Response Models in Randomized Experiments: Applications of the Resampling-Based Maximum Likelihood Method

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  • Takahiro ITO

    (Graduate School of International Cooperation Studies, Kobe University)

Abstract

This paper formulates a novel distribution-free maximum likelihood estimator for binary and ordered response models and demonstrates its finite sample performance in a Monte Carlo simulation. The simulation examines an ordered response model, focusing on estimating the effect of an exogenous regressor (e.g., randomly assigned treatment status) on the choice probability for an ordered outcome. Estimations are implemented based on a binary specification, which converts the outcome to dichotomous values {0, 1}, or an ordinal specification, which uses the outcome as is. The simulation results show that the proposed estimator outperforms conventional parametric/semiparametric estimators in most cases for both specifications. The results also show that the superiority of the proposed estimator holds even in the presence of conditionally heteroscedastic variance. In addition, the estimates based on the ordinal specification are always superior to those based on the binary specification in all simulation designs, implying that converting ordered responses to dichotomous responses and estimating based on the binary specification may not be the optimal approach.

Suggested Citation

  • Takahiro ITO, 2024. "Binary and Ordered Response Models in Randomized Experiments: Applications of the Resampling-Based Maximum Likelihood Method," GSICS Working Paper Series 42, Graduate School of International Cooperation Studies, Kobe University.
    • Handle: RePEc:kcs:wpaper:42
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    File URL: https://www.research.kobe-u.ac.jp/gsics-publication/gwps/2024-42.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    semiparametric estimation; distribution-free maximum likelihood; binary choice model; ordered response model; Likert-type data; heteroscedastic variance;
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