New Developments in Regression Discontinuity Designs: Covariates Adjustment and Coverage Optimal Inference

2022 年度 実施状況報告書

• 研究課題/領域番号: 21K01419
• 研究機関: 筑波大学
• 研究代表者: YU ZHENGFEI 筑波大学, 人文社会系, 准教授 (40774758)
• 研究期間 (年度): 2021-04-01 – 2025-03-31
• キーワード: Regression discontinuity / Empirical likelihood / Coverage error / Empirical likelihood / Local misspecification

研究実績の概要

This project proposes a novel approach to incorporate covariates in regression discontinuity (RD) designs. It represents the covariate balance condition as over-identifying moment restrictions. The empirical likelihood (EL) RD estimator efficiently incorporates the information from covariate balance and has an asymptotic variance no larger than that of the standard estimator without covariates. This project then proposes a robust corrected EL confidence set which achieves a faster coverage error decay rate. The coverage accuracy of the proposed confidence interval is automatically robust against slight perturbation to the covariate balance condition, which may happen in cases such as data contamination and misspecified “unaffected” outcomes used as covariates. This project also propose a way to conduct sensitivity analysis in the bandwidth choice when the researcher wants to use regression discontinuity with covariate adjustment. This project conducts Monte Carlo simulations to assess the finite-sample performance of the proposed inference method and then applies it to a real dataset.

現在までの達成度 (区分)

2: おおむね順調に進展している

理由

As planned, this research resolves the indeterminacy raised by Calonico, Cattaneo, Farrell, and Titiunik (2019, Page 448) regarding the asymptotic efficiency gain from incorporating covariates to RD estimator, by showing that RD estimator with covariate adjustment has an asymptotic variance no larger than that of the standard estimator without covariates. In addition, the proposed empirical likelihood inference method is shown to have theoretical advantages such as a faster coverage error decay rate and robustness to local specification. It also performs well in Monte Carlo simulations.

I presented the findings in conferences and received good feedbacks.

今後の研究の推進方策

The next steps for this project is to focus on the following aspects:
1. Give examples to illustrate that the moment restrictions framework for regression discontinuity applies to nonlinear models, such as categorical outcomes.
2. Connect the current work to the literature on balancing under unconfoundedness. The idea that balancing-type estimators can be an alternative to regression adjustments with many attractive properties has been explored in depth in the
literature on treatment effect estimation under confoundedness.

次年度使用額が生じた理由

I attended a hybrid conference using the online mode, so saved some travel expenses.

研究成果

• [国際共同研究] Renmin University of China/Chinese University of Hong Kong(中国)
  • 国名: 中国
  • 外国機関名: Renmin University of China/Chinese University of Hong Kong
• [国際共同研究] University of British Columbia(カナダ)
  • 国名: カナダ
  • 外国機関名: University of British Columbia
• [雑誌論文] Estimation and inference on treatment effects under treatment-based sampling designs (2022)
  • 著者名/発表者名: Song Kyungchul、Yu Zhengfei
  • 雑誌名: The Econometrics Journal 巻: 25 ページ: 554～575
  • DOI: 10.1093/ectj/utac008
  • 査読あり / 国際共著
• [雑誌論文] SIMPLE SEMIPARAMETRIC ESTIMATION OF ORDERED RESPONSE MODELS (2022)
  • 著者名/発表者名: Liu Ruixuan、Yu Zhengfei
  • 雑誌名: Econometric Theory 巻: - ページ: -
  • DOI: 10.1017/S0266466622000317
  • 査読あり / 国際共著
• [学会発表] Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs (2022)
  • 著者名/発表者名: Yu Zhengfei
  • 学会等名: 2022 Asian Meeting of the Econometric Society in East and South-East Asia
  • 国際学会