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EunYi Chung

Personal Details

First Name:EunYi
Middle Name:
Last Name:Chung
Suffix:
RePEc Short-ID:pch1615
[This author has chosen not to make the email address public]

Affiliation

Department of Economics
University of Illinois at Urbana-Champaign

Urbana-Champaign, Illinois (United States)
http://www.economics.illinois.edu/
RePEc:edi:deuiuus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Marinho Bertanha & EunYi Chung, 2021. "Permutation Tests at Nonparametric Rates," Papers 2102.13638, arXiv.org, revised Apr 2022.

Articles

  1. Marinho Bertanha & Eunyi Chung, 2023. "Permutation Tests at Nonparametric Rates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2833-2846, October.
  2. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
  3. Chung, EunYi & Romano, Joseph P., 2016. "Multivariate and multiple permutation tests," Journal of Econometrics, Elsevier, vol. 193(1), pages 76-91.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Marinho Bertanha & EunYi Chung, 2021. "Permutation Tests at Nonparametric Rates," Papers 2102.13638, arXiv.org, revised Apr 2022.

    Cited by:

    1. Marinho Bertanha & Andrew H. McCallum & Nathan Seegert, 2021. "Better Bunching, Nicer Notching," Finance and Economics Discussion Series 2021-002, Board of Governors of the Federal Reserve System (U.S.).

Articles

  1. Marinho Bertanha & Eunyi Chung, 2023. "Permutation Tests at Nonparametric Rates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2833-2846, October.
    See citations under working paper version above.
  2. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.

    Cited by:

    1. Young, Alwyn, 2024. "Asymptotically robust permutation-based randomization confidence intervals for parametric OLS regression," European Economic Review, Elsevier, vol. 163(C).
    2. Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jan 2024.
    3. Jeffrey Smith, 2022. "Treatment Effect Heterogeneity," Evaluation Review, , vol. 46(5), pages 652-677, October.

  3. Chung, EunYi & Romano, Joseph P., 2016. "Multivariate and multiple permutation tests," Journal of Econometrics, Elsevier, vol. 193(1), pages 76-91.

    Cited by:

    1. Young, Alwyn, 2024. "Asymptotically robust permutation-based randomization confidence intervals for parametric OLS regression," European Economic Review, Elsevier, vol. 163(C).
    2. Friedrich, Sarah & Pauly, Markus, 2018. "MATS: Inference for potentially singular and heteroscedastic MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 166-179.
    3. Federico A. Bugni & Joel L. Horowitz, 2018. "Permutation tests for equality of distributions of functional data," CeMMAP working papers CWP18/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Brendan K. Beare & Juwon Seo, 2019. "Randomization tests of copula symmetry," Papers 1911.05307, arXiv.org.
    5. James J. Heckman & Ganesh Karapakula, 2019. "The Perry Preschoolers at Late Midlife: A Study in Design-Specific Inference," Working Papers 2019-034, Human Capital and Economic Opportunity Working Group.
    6. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    7. Juwon Seo, 2018. "Randomization Tests for Equality in Dependence Structure," Papers 1811.02105, arXiv.org.
    8. Young, Alwyn, 2024. "Asymptotically robust permutation-based randomization confidence intervals for parametric OLS regression," LSE Research Online Documents on Economics 120933, London School of Economics and Political Science, LSE Library.
    9. Zeng-Hua Lu, 2019. "Extended MinP Tests of Multiple Hypotheses," Papers 1911.04696, arXiv.org.
    10. Ganesh Karapakula & James J. Heckman, 2020. "Using a Satisficing Model of Experimenter Decision-Making to Guide Finite-Sample Inference for Compromised Experiments," NBER Working Papers 27738, National Bureau of Economic Research, Inc.
    11. Purevdorj Tuvaandorj, 2021. "Robust Permutation Tests in Linear Instrumental Variables Regression," Papers 2111.13774, arXiv.org, revised Jun 2023.
    12. Ditzhaus, Marc & Smaga, Łukasz, 2022. "Permutation test for the multivariate coefficient of variation in factorial designs," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    13. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
    14. Dennis Dobler & Markus Pauly, 2018. "Bootstrap- and permutation-based inference for the Mann–Whitney effect for right-censored and tied data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 639-658, September.
    15. Konietschke, Frank & Bathke, Arne C. & Harrar, Solomon W. & Pauly, Markus, 2015. "Parametric and nonparametric bootstrap methods for general MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 291-301.
    16. Nick Koning & Paul Bekker, 2019. "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers 1904.12775, arXiv.org, revised Jun 2020.
    17. Peter L. Cohen & Colin B. Fogarty, 2022. "Gaussian prepivoting for finite population causal inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 295-320, April.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (1) 2021-03-08. Author is listed

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