Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods / Lourenço S. Paz, James E. West.

Format:

Book

Author/Creator:

Paz, Lourenço S.

Contributor:

National Bureau of Economic Research.

West, James E., 1965-

Series:

Working Paper Series (National Bureau of Economic Research) no. w25926.

NBER working paper series no. w25926

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2019.

Summary:

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.

Notes:

Print version record

June 2019.