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Optimal Inference in a Class of Regression Models

Citations

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Cited by:

  1. Bugni, Federico A. & Canay, Ivan A., 2021. "Testing continuity of a density via g-order statistics in the regression discontinuity design," Journal of Econometrics, Elsevier, vol. 221(1), pages 138-159.
  2. Bertanha, Marinho & Moreira, Marcelo J., 2020. "Impossible inference in econometrics: Theory and applications," Journal of Econometrics, Elsevier, vol. 218(2), pages 247-270.
  3. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
  4. Gregory Fletcher Cox, 2024. "A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality," Papers 2409.09962, arXiv.org.
  5. Atı̇la Abdulkadı̇roğlu & Joshua D. Angrist & Yusuke Narita & Parag Pathak, 2022. "Breaking Ties: Regression Discontinuity Design Meets Market Design," Econometrica, Econometric Society, vol. 90(1), pages 117-151, January.
  6. Timothy B. Armstrong & Michal Koles'ar & Mikkel Plagborg-M{o}ller, 2020. "Robust Empirical Bayes Confidence Intervals," Papers 2004.03448, arXiv.org, revised May 2022.
  7. Timothy B. Armstrong & Patrick Kline & Liyang Sun, 2023. "Adapting to Misspecification," Papers 2305.14265, arXiv.org, revised Aug 2024.
  8. Xiao Huang & Zhaoguo Zhan, 2022. "Local Composite Quantile Regression for Regression Discontinuity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1863-1875, October.
  9. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion Paper Series 730, Institute of Economic Research, Hitotsubashi University.
  10. Michael P. Leung, 2023. "Cluster-Randomized Trials with Cross-Cluster Interference," Papers 2310.18836, arXiv.org, revised Nov 2024.
  11. NARITA Yusuke & YATA Kohei, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion papers 21057, Research Institute of Economy, Trade and Industry (RIETI).
  12. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
  13. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
  14. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2023. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(3), pages 1977-2019.
  15. Paul Goldsmith-Pinkham & Karen Jiang & Zirui Song & Jacob Wallace, 2022. "Measuring Changes in Disparity Gaps: An Application to Health Insurance," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 356-360, May.
  16. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2019. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," NBER Working Papers 26562, National Bureau of Economic Research, Inc.
  17. Chenchuan (Mark) Li & Ulrich K. Müller, 2020. "Linear Regression with Many Controls of Limited Explanatory Power," Working Papers 2020-57, Princeton University. Economics Department..
  18. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
  19. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
  20. Feng, Jin & Song, Hong & Wang, Zhen, 2020. "The elderly's response to a patient cost-sharing policy in health insurance: Evidence from China," Journal of Economic Behavior & Organization, Elsevier, vol. 169(C), pages 189-207.
  21. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
  22. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
  23. Huynh, Nhan, 2023. "Unemployment beta and the cross-section of stock returns: Evidence from Australia," International Review of Financial Analysis, Elsevier, vol. 86(C).
  24. Yi Zhang & Eli Ben-Michael & Kosuke Imai, 2022. "Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs," Papers 2208.13323, arXiv.org, revised Sep 2024.
  25. Sasaki, Yuya & Wang, Yulong, 2024. "On uniform confidence intervals for the tail index and the extreme quantile," Journal of Econometrics, Elsevier, vol. 244(1).
  26. Myung Hwan Seo & Yoichi Arai & Taisuke Otsu, 2021. "Regression Discontinuity Design with Potentially Many Covariates," Working Paper Series no142, Institute of Economic Research, Seoul National University.
  27. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
  28. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
  29. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
  30. Walter Beckert & Daniel Kaliski, 2019. "Honest inference for discrete outcomes," CeMMAP working papers CWP67/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  31. Yiqi Liu & Yuan Qi, 2023. "Using Forests in Multivariate Regression Discontinuity Designs," Papers 2303.11721, arXiv.org, revised Jul 2024.
  32. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
  33. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
  34. Giuseppe Rose & Desiré De Luca, 2024. "Health Concerns And Consumption Expectations During Covid-19: Evidence From A Fuzzy Regression Discontinuity Design," Working Papers 202401, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
  35. Bai, Yuehao, 2023. "Why randomize? Minimax optimality under permutation invariance," Journal of Econometrics, Elsevier, vol. 232(2), pages 565-575.
  36. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
  37. Tuvaandorj, Purevdorj, 2020. "Regression discontinuity designs, white noise models, and minimax," Journal of Econometrics, Elsevier, vol. 218(2), pages 587-608.
  38. Alexander Kreiss & Christoph Rothe, 2023. "Inference in regression discontinuity designs with high-dimensional covariates," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 105-123.
  39. Yingying Dong & Michal Kolesár, 2023. "When can we ignore measurement error in the running variable?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 735-750, August.
  40. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
  41. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
  42. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
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