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Empirical likelihood confidence interval for difference-in-differences estimator with panel data

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  • Tang, Shengfang
  • Huang, Zhilin

Abstract

This paper develops an empirical likelihood approach to construct the confidence interval for the average treatment effect on the treated under the difference-in-differences framework. The empirical likelihood function is constructed based on a doubly robust moment function of the parameter of interest. Under some regularity conditions, we show that the proposed method retains the nonparametric Wilks property of empirical likelihood.

Suggested Citation

  • Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:ecolet:v:216:y:2022:i:c:s0165176522001392
    DOI: 10.1016/j.econlet.2022.110524
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    3. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    4. Neng-Chieh Chang, 2020. "Double/debiased machine learning for difference-in-differences models," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 177-191.
    5. Bravo, Francesco & Juan Carlos, Escanciano & Ingrid Van Keilegom, Ingrid, 2020. "Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Reprints ISBA 2020046, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    7. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    8. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    9. Jing Qin & And Biao Zhang, 2008. "Empirical‐likelihood‐based difference‐in‐differences estimators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 329-349, April.
    10. Li, Qi & Racine, Jeffrey S. & Wooldridge, Jeffrey M., 2009. "Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 206-223.
    11. Rothe, Christoph & Firpo, Sergio, 2019. "Properties Of Doubly Robust Estimators When Nuisance Functions Are Estimated Nonparametrically," Econometric Theory, Cambridge University Press, vol. 35(5), pages 1048-1087, October.
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    More about this item

    Keywords

    Empirical likelihood; Confidence interval; Difference-in-differences; Average treatment effect on the treated;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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