IDEAS home Printed from https://ideas.repec.org/h/spr/adschp/978-3-031-48385-1_9.html
   My bibliography  Save this book chapter

A simple, robust test for choosing the level of fixed effects in linear panel data models

In: Advances in Applied Econometrics

Author

Listed:
  • Leslie E. Papke

    (Michigan State University)

  • Jeffrey M. Wooldridge

    (Michigan State University)

Abstract

For the panel data case where cross-sectional units are nested within higher-level groups, and there are many such groups, we propose a test that allows one to determine whether controlling for fixed effects at the more aggregate level is sufficient. The alternative is that one should allow for fixed effects at the unit level. The regression-based test is simple to carry out, even for unbalanced panels. In addition, the test is easily made robust to arbitrary heteroskedasticity, serial correlation across time, and even cluster correlation at the group level. We also show how to modify the traditional Hausman test of a single coefficient to be fully robust to serial correlation and cluster correlation. The tests work well in terms of size and power in a small simulation study. We apply the test to choosing between a fixed effects analysis at the school district level and the disaggregated school level.

Suggested Citation

  • Leslie E. Papke & Jeffrey M. Wooldridge, 2024. "A simple, robust test for choosing the level of fixed effects in linear panel data models," Advanced Studies in Theoretical and Applied Econometrics, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang (ed.), Advances in Applied Econometrics, pages 219-237, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-48385-1_9
    DOI: 10.1007/978-3-031-48385-1_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    2. 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.
    3. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    4. Jeffrey M. Wooldridge, 2003. "Cluster-Sample Methods in Applied Econometrics," American Economic Review, American Economic Association, vol. 93(2), pages 133-138, May.
    5. Yang, Yimin & Schmidt, Peter, 2021. "An econometric approach to the estimation of multi-level models," Journal of Econometrics, Elsevier, vol. 220(2), pages 532-543.
    6. 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.
    7. Wooldridge, Jeffrey M., 2019. "Correlated random effects models with unbalanced panels," Journal of Econometrics, Elsevier, vol. 211(1), pages 137-150.
    8. Papke, Leslie E. & Wooldridge, Jeffrey M., 2008. "Panel data methods for fractional response variables with an application to test pass rates," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 121-133, July.
    9. Papke, Leslie E., 2005. "The effects of spending on test pass rates: evidence from Michigan," Journal of Public Economics, Elsevier, vol. 89(5-6), pages 821-839, June.
    10. Riju Joshi & Jeffrey M. Wooldridge, 2019. "Correlated Random Effects Models with Endogenous Explanatory Variables and Unbalanced Panels," Annals of Economics and Statistics, GENES, issue 134, pages 243-268.
    11. Guggenberger, Patrik, 2010. "The impact of a Hausman pretest on the size of a hypothesis test: The panel data case," Journal of Econometrics, Elsevier, vol. 156(2), pages 337-343, June.
    12. Guggenberger, Patrik, 2010. "The Impact Of A Hausman Pretest On The Asymptotic Size Of A Hypothesis Test," Econometric Theory, Cambridge University Press, vol. 26(2), pages 369-382, April.
    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. Hassan Sherry & Hussein Zeaiter, 2024. "IMF Conditionality and Government Education Spending: The Case of 10 MENA Countries," Economies, MDPI, vol. 12(9), pages 1-23, August.
    2. Claudia Pigini & Alessandro Pionati & Francesco Valentini, 2023. "Specification testing with grouped fixed effects," Papers 2310.01950, arXiv.org.

    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. Arnd Kölling & Claus Schnabel, 2022. "Owners, external managers and industrial relations in German establishments," British Journal of Industrial Relations, London School of Economics, vol. 60(2), pages 424-443, June.
    2. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    3. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Jan 2025.
    4. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.
    5. Wooldridge, Jeffrey M., 2019. "Correlated random effects models with unbalanced panels," Journal of Econometrics, Elsevier, vol. 211(1), pages 137-150.
    6. Michael D. Bates & Leslie E. Papke & Jeffrey M. Wooldridge, 2024. "Non linear correlated random effects models with endogeneity and unbalanced panels," Econometric Reviews, Taylor & Francis Journals, vol. 43(9), pages 713-732, October.
    7. Jackie M. L. Chan & Huanhuan Zheng, 2022. "FDI on the move: cross-border M&A and migrant networks," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 947-985, August.
    8. Woutersen, Tiemen & Hausman, Jerry A., 2019. "Increasing the power of specification tests," Journal of Econometrics, Elsevier, vol. 211(1), pages 166-175.
    9. Chatelain, Jean-Bernard & Ralf, Kirsten, 2021. "Inference on time-invariant variables using panel data: A pretest estimator," Economic Modelling, Elsevier, vol. 97(C), pages 157-166.
    10. Becker, Annette & Hottenrott, Hanna & Mukherjee, Anwesha, 2022. "Division of labor in R&D? Firm size and specialization in corporate research," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 1-23.
    11. Thomas Slijper & Yann de Mey & P Marijn Poortvliet & Miranda P M Meuwissen, 2022. "Quantifying the resilience of European farms using FADN," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 121-150.
    12. KOUAKOU, Dorgyles C.M. & SZEGO, Eva, 2024. "Evaluating the integration of artificial intelligence technologies in defense activities and the effect of national innovation system performance on its enhancement," MPRA Paper 120617, University Library of Munich, Germany.
    13. Arnd Kölling, 2022. "Monopsony power and the demand for low-skilled workers," The Economic and Labour Relations Review, , vol. 33(2), pages 377-395, June.
    14. Seonho Shin, 2021. "Were they a shock or an opportunity?: The heterogeneous impacts of the 9/11 attacks on refugees as job seekers—a nonlinear multi-level approach," Empirical Economics, Springer, vol. 61(5), pages 2827-2864, November.
    15. Antonella Francesca Cicchiello & Serena Gallo & Stefano Monferrà, 2023. "Financing the cultural and creative industries through crowdfunding: the role of national cultural dimensions and policies," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 47(1), pages 133-175, March.
    16. Kabaila, Paul & Mainzer, Rheanna & Farchione, Davide, 2015. "The impact of a Hausman pretest, applied to panel data, on the coverage probability of confidence intervals," Economics Letters, Elsevier, vol. 131(C), pages 12-15.
    17. Gasmi,Farid & Kouakou,Dorgyles Christ Maurel & Noumba Um,Paul & Milla,Pedro Rojas, 2023. "An Empirical Analysis of the Social Contract in the Middle East and North Africa : Regionand the Role of Digitalization in Its Transformation," Policy Research Working Paper Series 10455, The World Bank.
    18. Makate, Clifton & Angelsen, Arild & Holden, Stein Terje & Westengen, Ola Tveitereid, 2022. "Crops in crises: Shocks shape smallholders' diversification in rural Ethiopia," World Development, Elsevier, vol. 159(C).
    19. Pronti, A. & Zegarra, E. & Vicario, D. Rey & Graves, A., 2024. "Global exports draining local water resources: Land concentration, food exports and water grabbing in the Ica Valley (Peru)," World Development, Elsevier, vol. 177(C).
    20. 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.

    More about this item

    Keywords

    Panel data; Fixed effects; Correlated random effects; Hausman test;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • 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:spr:adschp:978-3-031-48385-1_9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.