IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2002.02097.html
   My bibliography  Save this paper

Dependence-Robust Inference Using Resampled Statistics

Author

Listed:
  • Michael P. Leung

Abstract

We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well-known forms of weak dependence and justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.

Suggested Citation

  • Michael P. Leung, 2020. "Dependence-Robust Inference Using Resampled Statistics," Papers 2002.02097, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2002.02097
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2002.02097
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Shuyang Sheng, 2020. "A Structural Econometric Analysis of Network Formation Games Through Subnetworks," Econometrica, Econometric Society, vol. 88(5), pages 1829-1858, September.
    2. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781108400008.
    3. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    4. Rustam Ibragimov & Ulrich K. Müller, 2016. "Inference with Few Heterogeneous Clusters," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 83-96, March.
    5. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    6. Daron Acemoglu & Camilo García-Jimeno & James A. Robinson, 2015. "State Capacity and Economic Development: A Network Approach," American Economic Review, American Economic Association, vol. 105(8), pages 2364-2409, August.
    7. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781316510520.
    8. Michael P. Leung & Hyungsik Roger Moon, 2019. "Normal Approximation in Large Network Models," Papers 1904.11060, arXiv.org, revised Feb 2023.
    9. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    10. Thomas Barrios & Rebecca Diamond & Guido W. Imbens & Michal Kolesár, 2012. "Clustering, Spatial Correlations, and Randomization Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 578-591, June.
    11. Bester, C. Alan & Conley, Timothy G. & Hansen, Christian B., 2011. "Inference with dependent data using cluster covariance estimators," Journal of Econometrics, Elsevier, vol. 165(2), pages 137-151.
    12. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781108414982.
    13. Pesaran, M. Hashem, 1987. "Global and Partial Non-Nested Hypotheses and Asymptotic Local Power," Econometric Theory, Cambridge University Press, vol. 3(1), pages 69-97, February.
    14. Ivan A. Canay & Joseph P. Romano & Azeem M. Shaikh, 2017. "Randomization Tests Under an Approximate Symmetry Assumption," Econometrica, Econometric Society, vol. 85, pages 1013-1030, May.
    15. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    16. Michael P. Leung, 2020. "Treatment and Spillover Effects Under Network Interference," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 368-380, May.
    17. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    18. Andreas Klaus & Shan Yu & Dietmar Plenz, 2011. "Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
    19. Leung, Michael P., 2019. "A weak law for moments of pairwise stable networks," Journal of Econometrics, Elsevier, vol. 210(2), pages 310-326.
    20. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    21. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781108400022.
    22. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.
    23. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, vol. 97(3), pages 890-915, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    2. Bruno Ferman, 2023. "Inference in difference‐in‐differences: How much should we trust in independent clusters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 358-369, April.
    3. Hwang, Jungbin, 2021. "Simple and trustworthy cluster-robust GMM inference," Journal of Econometrics, Elsevier, vol. 222(2), pages 993-1023.
    4. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    5. Hagemann, Andreas, 2019. "Placebo inference on treatment effects when the number of clusters is small," Journal of Econometrics, Elsevier, vol. 213(1), pages 190-209.
    6. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
    7. Wang, Wenjie, 2021. "Wild Bootstrap for Instrumental Variables Regression with Weak Instruments and Few Clusters," MPRA Paper 106227, University Library of Munich, Germany.
    8. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    9. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Testing for the appropriate level of clustering in linear regression models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2027-2056.
    10. Yoshiyuki ARATA & Philipp MUNDT, 2019. "Topology and Formation of Production Input Interlinkages: Evidence from Japanese microdata," Discussion papers 19027, Research Institute of Economy, Trade and Industry (RIETI).
    11. Yong Cai, 2021. "A Modified Randomization Test for the Level of Clustering," Papers 2105.01008, arXiv.org, revised Jan 2022.
    12. MacKinnon, James G. & Webb, Matthew D., 2020. "Randomization inference for difference-in-differences with few treated clusters," Journal of Econometrics, Elsevier, vol. 218(2), pages 435-450.
    13. Gualdani, Cristina, 2021. "An econometric model of network formation with an application to board interlocks between firms," Journal of Econometrics, Elsevier, vol. 224(2), pages 345-370.
    14. Andreas Hagemann, 2019. "Permutation inference with a finite number of heterogeneous clusters," Papers 1907.01049, arXiv.org, revised Feb 2023.
    15. Jungbin Hwang, 2017. "Simple and Trustworthy Cluster-Robust GMM Inference," Working papers 2017-19, University of Connecticut, Department of Economics, revised Aug 2020.
    16. Tadao Hoshino & Daichi Shimamoto & Yasuyuki Todo, 2020. "Accounting for Heterogeneity in Network Formation Behaviour: An Application to Vietnamese SMEs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(5), pages 1042-1067, October.
    17. Tom Boot & Gianmaria Niccodemi & Tom Wansbeek, 2023. "Unbiased estimation of the OLS covariance matrix when the errors are clustered," Empirical Economics, Springer, vol. 64(6), pages 2511-2533, June.
    18. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2018. "The wild bootstrap with a "small" number of "large" clusters," CeMMAP working papers CWP27/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Yong Cai, 2021. "Panel Data with Unknown Clusters," Papers 2106.05503, arXiv.org, revised Jan 2022.
    20. Chih-Sheng Hsieh & Stanley I. M. Ko & Jaromír Kovářík & Trevon Logan, 2018. "Non-Randomly Sampled Networks: Biases and Corrections," NBER Working Papers 25270, National Bureau of Economic Research, Inc.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2002.02097. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.