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Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods

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  • Lourenço S. Paz
  • James E. West

Abstract

We compare the precision of critical values obtained under conventional sampling-based methods with those obtained using sample order statics computed through draws from a randomized counterfactual based on the null hypothesis. When based on a small number of draws (200), critical values in the extreme left and right tail (0.005 and 0.995) contain a small bias toward failing to reject the null hypothesis which quickly dissipates with additional draws. The precision of randomization-based critical values compares favorably with conventional sampling-based critical values when the number of draws is approximately 7 times the sample size for a basic OLS model using homoskedastic data, but considerably less in models based on clustered standard errors, or the classic Differences-in-Differences. Randomization-based methods dramatically outperform conventional methods for treatment effects in Differences-in-Differences specifications with unbalanced panels and a small number of treated groups.

Suggested Citation

  • Lourenço S. Paz & James E. West, 2019. "Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods," NBER Working Papers 25926, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:25926
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    1. repec:pri:rpdevs:gamespaper.pdf is not listed on IDEAS
    2. 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.
    3. 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.
    4. Raj Chetty & Adam Looney & Kory Kroft, 2009. "Salience and Taxation: Theory and Evidence," American Economic Review, American Economic Association, vol. 99(4), pages 1145-1177, September.
    5. Dean S. Karlan, 2005. "Using Experimental Economics to Measure Social Capital and Predict Financial Decisions," American Economic Review, American Economic Association, vol. 95(5), pages 1688-1699, December.
    6. Abigail Barr, 2003. "Trust and expected trustworthiness: experimental evidence from zimbabwean villages," Economic Journal, Royal Economic Society, vol. 113(489), pages 614-630, July.
    7. Timothy G. Conley & Christopher R. Taber, 2011. "Inference with "Difference in Differences" with a Small Number of Policy Changes," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 113-125, February.
    8. Scott E. Carrell & Bruce I. Sacerdote & James E. West, 2013. "From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation," Econometrica, Econometric Society, vol. 81(3), pages 855-882, May.
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    Cited by:

    1. Aimone, Jason A. & Ward, Brittany & West, James E., 2020. "Dishonest behavior: Sin big or go home," Economics Letters, Elsevier, vol. 186(C).
    2. Breda, Thomas & Grenet, Julien & Monnet, Marion & Van Effenterre, Clémentine, 2020. "Do Female Role Models Reduce the Gender Gap in Science? Evidence from French High Schools," IZA Discussion Papers 13163, Institute of Labor Economics (IZA).
    3. Debdeep Chattopadhyay, 2023. "Did the Massachusetts Health Reform Program increase self-employment?," Empirical Economics, Springer, vol. 65(3), pages 1309-1344, September.

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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