IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v145y2016icp221-224.html
   My bibliography  Save this article

An equicorrelation Moulton factor in the presence of arbitrary intra-cluster correlation

Author

Listed:
  • Montes-Rojas, Gabriel

Abstract

This note highlights the potential pitfalls of using an equicorrelation model to estimate standard errors when the true model has arbitrary intra-cluster correlation. It derives a generalized equicorrelation Moulton factor that quantifies the potential biases in standard errors for OLS estimators. As with the famous Moulton factor, the key role is not played by the correlation of the error terms but rather by the intra-correlation of the covariates themselves.

Suggested Citation

  • Montes-Rojas, Gabriel, 2016. "An equicorrelation Moulton factor in the presence of arbitrary intra-cluster correlation," Economics Letters, Elsevier, vol. 145(C), pages 221-224.
    • Handle: RePEc:eee:ecolet:v:145:y:2016:i:c:p:221-224
    DOI: 10.1016/j.econlet.2016.06.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176516302269
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2016.06.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. Moulton, Brent R, 1987. "Diagnostics for Group Effects in Regression Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 275-282, April.
    4. 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.
    5. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    6. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    7. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    8. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-338, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Montes-Rojas Gabriel, 2022. "Subgraph Network Random Effects Error Components Models: Specification and Testing," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 17-34, January.
    2. Alejo, Javier & Montes-Rojas, Gabriel & Sosa-Escudero, Walter, 2018. "Testing for serial correlation in hierarchical linear models," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 101-116.

    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. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    2. Aleksey Oshchepkov & Anna Shirokanova, 2020. "Multilevel Modeling For Economists: Why, When And How," HSE Working papers WP BRP 233/EC/2020, National Research University Higher School of Economics.
    3. Marcelo Moreira & Geert Ridder, 2019. "Efficiency loss of asymptotically efficient tests in an instrumental variables regression," CeMMAP working papers CWP03/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    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. Rok Spruk & Mitja Kovac, 2018. "Inefficient Growth," Review of Economics and Institutions, Università di Perugia, vol. 9(2).
    6. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    7. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    8. 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.
    9. Joshua D. Angrist & Jörn-Steffen Pischke, 2017. "Undergraduate Econometrics Instruction: Through Our Classes, Darkly," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 125-144, Spring.
    10. 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.
    11. Mitja Kovac & Salvini Datta & Rok Spruk, 2021. "Pharmaceutical Product Liability, Litigation Regimes, and the Propensity to Patent: An Empirical Firm-Level Investigation," SAGE Open, , vol. 11(2), pages 21582440211, April.
    12. Miroslav Verbič & Rok Spruk, 2019. "Political economy of pension reforms: an empirical investigation," European Journal of Law and Economics, Springer, vol. 47(2), pages 171-232, April.
    13. Giuseppe Moscelli & Hugh Gravelle & Luigi Siciliani, 2018. "Effects of Market Structure and Patient Choice on Hospital Quality for Planned Patients," School of Economics Discussion Papers 1118, School of Economics, University of Surrey.
    14. 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.
    15. Jeffrey R. Vincent & Ismariah Ahmad & Norliyana Adnan & Walter B. Burwell & Subhrendu K. Pattanayak & Jie-Sheng Tan-Soo & Kyle Thomas, 2016. "Valuing Water Purification by Forests: An Analysis of Malaysian Panel Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(1), pages 59-80, May.
    16. Entorf, Horst & Sattarova, Liliya, 2016. "The Analysis of Prison-Prisoner Data Using Cluster-Sample Econometrics: Prison Conditions and Prisoners' Assessments of the Future," IZA Discussion Papers 10209, Institute of Labor Economics (IZA).
    17. Thomas K. Bauer & Tanja Kasten & Lars-H. R. Siemers, 2017. "Business Taxation and Wages: Redistribution and Asymmetric Effects," MAGKS Papers on Economics 201732, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Antoine A. Djogbenou & James G. MacKinnon & Morten Ø. Nielsen, 2017. "Validity Of Wild Bootstrap Inference With Clustered Errors," Working Paper 1383, Economics Department, Queen's University.
    19. 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.
    20. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.

    More about this item

    Keywords

    Microeconometrics; Clusters; Aggregate variables; Moulton factor;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

    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:eee:ecolet:v:145:y:2016:i:c:p:221-224. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.