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When should we adjust standard errors for clustering?
1 Referee 1 tells you “the wage residual is likely to be correlated within local labor markets, so you should cluster your standard errors by state or village.” 3 Referee 3 argues that “the wage residual is likely to be correlated by age cohort, so you should cluster your standard errors by cohort”.
What is the effect of clustering standard errors?
Intuitive Motivation. Clustered standard errors are often useful when treatment is assigned at the level of a cluster instead of at the individual level. For example, suppose that an educational researcher wants to discover whether a new teaching technique improves student test scores.
However, autocorrelated standard errors render the usual homoskedasticity-only and heteroskedasticity-robust standard errors invalid and may cause misleading inference. HAC errors are a remedy.
How to deal with the problem of autocorrelation?
There are two ways of dealing with the problem of autocorrelated errors. Leave the model specification as is but expand confidence intervals around the regression coefficients to account for the violation of the model assumption of non-autocorrelated errors.
Which is the best description of standard error?
The standard deviation describes variability within a single sample. The standard error estimates the variability across multiple samples of a population. The standard deviation is a descriptive statistic that can be calculated from sample data.
When do you not need to cluster standard errors?
You want to say something about the association between schooling and wages in a particular population, and are using a random sample of workers from this population. Then there is no need to adjust the standard errors for clustering at all, even if clustering would change the standard errors.