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Max Tabord-Meehan

Personal Details

First Name:Max
Middle Name:
Last Name:Tabord-Meehan
Suffix:
RePEc Short-ID:pta787
[This author has chosen not to make the email address public]

Affiliation

Department of Economics
University of Chicago

Chicago, Illinois (United States)
http://economics.uchicago.edu/
RePEc:edi:deuchus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Eric Auerbach & Jonathan Auerbach & Max Tabord-Meehan, 2024. "Exposure effects are not automatically useful for policymaking," Papers 2401.06264, arXiv.org, revised Jan 2024.
  2. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
  3. Eric Auerbach & Annie Liang & Kyohei Okumura & Max Tabord-Meehan, 2024. "Testing the Fairness-Accuracy Improvability of Algorithms," Papers 2405.04816, arXiv.org, revised Jul 2024.
  4. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.
  5. Yuehao Bai & Hongchang Guo & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "Inference in Experiments with Matched Pairs and Imperfect Compliance," Papers 2307.13094, arXiv.org, revised Jun 2024.
  6. Federico Bugni & Ivan Canay & Azeem Shaikh & Max Tabord-Meehan, 2022. "Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes," Papers 2204.08356, arXiv.org, revised Apr 2024.
  7. Yuehao Bai & Meng Hsuan Hsieh & Jizhou Liu & Max Tabord-Meehan, 2022. "Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition," Papers 2209.11840, arXiv.org, revised Oct 2023.
  8. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.
  9. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2022. "Inference in Cluster Randomized Trials with Matched Pairs," Papers 2211.14903, arXiv.org, revised Aug 2024.
  10. Eric Auerbach & Max Tabord-Meehan, 2021. "The Local Approach to Causal Inference under Network Interference," Papers 2105.03810, arXiv.org, revised Jun 2023.
  11. Max Tabord-Meehan, 2018. "Stratification Trees for Adaptive Randomization in Randomized Controlled Trials," Papers 1806.05127, arXiv.org, revised Jul 2022.
  12. Eric Mbakop & Max Tabord-Meehan, 2016. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Papers 1609.03167, arXiv.org, revised Dec 2020.

Articles

  1. Eric Auerbach & Jonathan Auerbach & Max Tabord-Meehan, 2024. "Discussion of ‘Causal inference with misspecified exposure mappings: separating definitions and assumptions’," Biometrika, Biometrika Trust, vol. 111(1), pages 21-24.
  2. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
  3. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
  4. Max Tabord-Meehan, 2019. "Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-Statistic," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 671-680, October.
  5. Charles F. Manski & Max Tabord-Meehan, 2017. "Evaluating the maximum MSE of mean estimators with missing data," Stata Journal, StataCorp LP, vol. 17(3), pages 723-735, September.

Software components

  1. Chuck Manski & Max Tabord-Meehan, 2017. "WALD_MSE: Stata module to calculate the maximum mean square error (MSE) of a point estimator of the mean," Statistical Software Components S458388, Boston College Department of Economics.

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. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.

    Cited by:

    1. Laurent Davezies & Guillaume Hollard & Pedro Vergara Merino, 2024. "Revisiting Randomization with the Cube Method," Papers 2407.13613, arXiv.org.
    2. Fang Han, 2024. "An Introduction to Permutation Processes (version 0.5)," Papers 2407.09664, arXiv.org.

  2. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.

    Cited by:

    1. Max Cytrynbaum, 2024. "Finely Stratified Rerandomization Designs," Papers 2407.03279, arXiv.org, revised Jul 2024.
    2. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
    3. Bai, Yuehao & Jiang, Liang & Romano, Joseph P. & Shaikh, Azeem M. & Zhang, Yichong, 2024. "Covariate adjustment in experiments with matched pairs," Journal of Econometrics, Elsevier, vol. 241(1).
    4. Yuehao Bai & Hongchang Guo & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "Inference in Experiments with Matched Pairs and Imperfect Compliance," Papers 2307.13094, arXiv.org, revised Jun 2024.

  3. Yuehao Bai & Hongchang Guo & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "Inference in Experiments with Matched Pairs and Imperfect Compliance," Papers 2307.13094, arXiv.org, revised Jun 2024.

    Cited by:

    1. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.
    2. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.

  4. Federico Bugni & Ivan Canay & Azeem Shaikh & Max Tabord-Meehan, 2022. "Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes," Papers 2204.08356, arXiv.org, revised Apr 2024.

    Cited by:

    1. Jizhou Liu, 2023. "Inference for Two-stage Experiments under Covariate-Adaptive Randomization," Papers 2301.09016, arXiv.org, revised Oct 2024.

  5. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.

    Cited by:

    1. Yuehao Bai & Meng Hsuan Hsieh & Jizhou Liu & Max Tabord-Meehan, 2022. "Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition," Papers 2209.11840, arXiv.org, revised Oct 2023.
    2. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.
    3. Bai, Yuehao & Jiang, Liang & Romano, Joseph P. & Shaikh, Azeem M. & Zhang, Yichong, 2024. "Covariate adjustment in experiments with matched pairs," Journal of Econometrics, Elsevier, vol. 241(1).
    4. Jizhou Liu, 2023. "Inference for Two-stage Experiments under Covariate-Adaptive Randomization," Papers 2301.09016, arXiv.org, revised Oct 2024.
    5. Yuehao Bai & Hongchang Guo & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "Inference in Experiments with Matched Pairs and Imperfect Compliance," Papers 2307.13094, arXiv.org, revised Jun 2024.
    6. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2022. "Inference in Cluster Randomized Trials with Matched Pairs," Papers 2211.14903, arXiv.org, revised Aug 2024.

  6. Eric Auerbach & Max Tabord-Meehan, 2021. "The Local Approach to Causal Inference under Network Interference," Papers 2105.03810, arXiv.org, revised Jun 2023.

    Cited by:

    1. Michael P. Leung, 2024. "Identifying Treatment and Spillover Effects Using Exposure Contrasts," Papers 2403.08183, arXiv.org, revised Dec 2024.
    2. Anish Agarwal & Sarah H. Cen & Devavrat Shah & Christina Lee Yu, 2022. "Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference," Papers 2210.11355, arXiv.org, revised Oct 2023.
    3. Alejandro Sanchez-Becerra, 2022. "The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment," Papers 2209.14391, arXiv.org.

  7. Max Tabord-Meehan, 2018. "Stratification Trees for Adaptive Randomization in Randomized Controlled Trials," Papers 1806.05127, arXiv.org, revised Jul 2022.

    Cited by:

    1. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jul 2022.
    2. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
    3. Rosenman Evan T. R. & Owen Art B., 2021. "Designing experiments informed by observational studies," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 147-171, January.
    4. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    5. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," American Economic Review, American Economic Association, vol. 112(12), pages 3911-3940, December.
    6. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2023. "Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds," Papers 2302.02988, arXiv.org, revised Jul 2023.
    7. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2017. "Inference under covariate-adaptive randomization with multiple treatments," CeMMAP working papers CWP34/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Liang Jiang & Xiaobin Liu & Peter C.B. Phillips & Yichong Zhang, 2020. "Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs," Cowles Foundation Discussion Papers 2249, Cowles Foundation for Research in Economics, Yale University.
    9. Ahnaf Rafi, 2023. "Efficient Semiparametric Estimation of Average Treatment Effects Under Covariate Adaptive Randomization," Papers 2305.08340, arXiv.org.
    10. Bugni, Federico A. & Gao, Mengsi, 2023. "Inference under covariate-adaptive randomization with imperfect compliance," Journal of Econometrics, Elsevier, vol. 237(1).
    11. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    12. Harrison H. Li & Art B. Owen, 2023. "Double machine learning and design in batch adaptive experiments," Papers 2309.15297, arXiv.org.
    13. Masahiro Kato & Akihiro Oga & Wataru Komatsubara & Ryo Inokuchi, 2024. "Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices," Papers 2403.03589, arXiv.org, revised Jun 2024.
    14. Yichong Zhang & Xin Zheng, 2020. "Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization," Quantitative Economics, Econometric Society, vol. 11(3), pages 957-982, July.
    15. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.
    16. Federico A. Bugni & Mengsi Gao, 2021. "Inference under Covariate-Adaptive Randomization with Imperfect Compliance," Papers 2102.03937, arXiv.org, revised Jul 2023.
    17. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Jan 2024.
    18. Masahiro Kato, 2023. "Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budget," Papers 2310.19788, arXiv.org, revised Mar 2024.

  8. Eric Mbakop & Max Tabord-Meehan, 2016. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Papers 1609.03167, arXiv.org, revised Dec 2020.

    Cited by:

    1. Manski, Charles F., 2023. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, Elsevier, vol. 234(2), pages 647-663.
    2. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
    3. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    4. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    5. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    6. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
    7. Daniel F. Pellatt, 2022. "PAC-Bayesian Treatment Allocation Under Budget Constraints," Papers 2212.09007, arXiv.org, revised Jun 2023.
    8. Daido Kido, 2023. "Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification," Papers 2311.08958, arXiv.org.
    9. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    10. Yuya Sasaki & Takuya Ura, 2020. "Welfare Analysis via Marginal Treatment Effects," Papers 2012.07624, arXiv.org.
    11. Takuya Ishihara & Toru Kitagawa, 2021. "Evidence Aggregation for Treatment Choice," Papers 2108.06473, arXiv.org, revised Jul 2024.
    12. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Aug 2024.
    13. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
    14. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for Continuous Treatments," Papers 2402.02535, arXiv.org, revised Nov 2024.
    15. Daido Kido, 2023. "Incorporating Preferences Into Treatment Assignment Problems," Papers 2311.08963, arXiv.org.
    16. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2023. "Treatment Choice, Mean Square Regret and Partial Identification," Papers 2310.06242, arXiv.org.
    17. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
    18. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
    19. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2022. "Treatment Choice with Nonlinear Regret," Papers 2205.08586, arXiv.org, revised Oct 2024.
    20. Toru Kitagawa & Guanyi Wang, 2020. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP59/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Toru Kitagawa & Jeff Rowley, 2024. "Bandit algorithms for policy learning: methods, implementation, and welfare-performance," The Japanese Economic Review, Springer, vol. 75(3), pages 407-447, July.
    22. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    23. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare," Papers 2410.07443, arXiv.org, revised Oct 2024.
    24. Federico Crippa, 2024. "Regret Analysis in Threshold Policy Design," Papers 2404.11767, arXiv.org.
    25. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    26. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
    27. Charles F. Manski, 2019. "Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald," Papers 1912.08726, arXiv.org, revised Feb 2021.
    28. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Discrimination in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org.
    29. Hugo Bodory & Federica Mascolo & Michael Lechner, 2024. "Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment," Papers 2406.02241, arXiv.org.
    30. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    31. Daido Kido, 2022. "Distributionally Robust Policy Learning with Wasserstein Distance," Papers 2205.04637, arXiv.org, revised Aug 2022.
    32. Toru Kitagawa & Weining Wang & Mengshan Xu, 2022. "Policy Choice in Time Series by Empirical Welfare Maximization," Papers 2205.03970, arXiv.org, revised Jun 2023.
    33. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    34. Ryo Okui, 2024. "The 2023 Japanese Economic Association Nakahara Prize: Recipient—Prof. Toru Kitagawa, Brown University and University College London," The Japanese Economic Review, Springer, vol. 75(3), pages 405-406, July.
    35. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    36. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    37. Le-Yu Chen & Sokbae Lee, 2018. "High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization," Papers 1811.09540, arXiv.org.
    38. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Jul 2023.
    39. Seungjin Han & Julius Owusu & Youngki Shin, 2022. "Statistical Treatment Rules under Social Interaction," Papers 2209.09077, arXiv.org, revised Nov 2022.
    40. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
    41. Liyang Sun, 2021. "Empirical Welfare Maximization with Constraints," Papers 2103.15298, arXiv.org, revised Sep 2024.

Articles

  1. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
    See citations under working paper version above.
  2. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    See citations under working paper version above.
  3. Max Tabord-Meehan, 2019. "Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-Statistic," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 671-680, October.

    Cited by:

    1. Nicola Campigotto & Chiara Rapallini & Aldo Rustichini, 2022. "School friendship networks, homophily and multiculturalism: evidence from European countries," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1687-1722, October.
    2. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2023. "Inference for High-Dimensional Exchangeable Arrays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1595-1605, July.
    3. Áureo de Paula, 2020. "Econometric Models of Network Formation," CeMMAP working papers CWP4/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Bryan S. Graham, 2019. "Network Data," Papers 1912.06346, arXiv.org.
    5. Fe, Hao, 2023. "Social networks and consumer behavior: Evidence from Yelp," Journal of Economic Behavior & Organization, Elsevier, vol. 209(C), pages 1-14.
    6. Luis E. Candelaria & Yichong Zhang, 2024. "Robust Inference in Locally Misspecified Bipartite Networks," Papers 2403.13725, arXiv.org.
    7. Bryan S. Graham, 2019. "Dyadic Regression," Papers 1908.09029, arXiv.org.
    8. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    9. Putman, Daniel S., 2020. "The Scope of Risk Pooling," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304480, Agricultural and Applied Economics Association.
    10. Bruce E. Hansen & Seojeong Lee, 2019. "Asymptotic Theory for Clustered Samples," Papers 1902.01497, arXiv.org.
    11. Konrad Menzel, 2021. "Bootstrap With Cluster‐Dependence in Two or More Dimensions," Econometrica, Econometric Society, vol. 89(5), pages 2143-2188, September.
    12. Nathan Canen & Ko Sugiura, 2022. "Inference in Linear Dyadic Data Models with Network Spillovers," Papers 2203.03497, arXiv.org, revised Jun 2023.
    13. Harold D Chiang & Yuya Sasaki, 2023. "On Using The Two-Way Cluster-Robust Standard Errors," Papers 2301.13775, arXiv.org.
    14. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  4. Charles F. Manski & Max Tabord-Meehan, 2017. "Evaluating the maximum MSE of mean estimators with missing data," Stata Journal, StataCorp LP, vol. 17(3), pages 723-735, September.

    Cited by:

    1. Charles F. Manski, 2019. "Statistical inference for statistical decisions," Papers 1909.06853, arXiv.org.
    2. Charles F. Manski, 2019. "Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald," Papers 1912.08726, arXiv.org, revised Feb 2021.

Software components

    Sorry, no citations of software components recorded.

More information

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Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 10 papers 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 (8) 2018-06-25 2021-05-17 2022-05-16 2022-07-25 2022-10-31 2023-01-09 2023-08-28 2023-08-28. Author is listed
  2. NEP-EXP: Experimental Economics (8) 2018-06-25 2022-05-16 2022-07-25 2022-10-31 2023-01-09 2023-08-28 2023-08-28 2024-06-10. Author is listed
  3. NEP-AIN: Artificial Intelligence (1) 2024-06-17
  4. NEP-CDM: Collective Decision-Making (1) 2021-05-17
  5. NEP-NET: Network Economics (1) 2021-05-17
  6. NEP-URE: Urban and Real Estate Economics (1) 2021-05-17

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