What is the Moulton factor?
The square root of the Moulton factor measures how much the usual OLS standard errors understate the correct standard errors. For example, with cluster size M = 500 and intracluster correlations ρu = 0.1 and ρx = 0.1, the correct standard errors are 2.45 times the usual OLS ones. Lessons from the Moulton factor.
Do clustered standard errors account for Heteroskedasticity?
Clustered standard errors are a special kind of robust standard errors that account for heteroskedasticity across “clusters” of observations (such as states, schools, or individuals). Clustered standard errors are generally recommended when analyzing panel data, where each unit is observed across time.
How are clustered standard errors used in economic theory?
Clustered standard errors are a way to obtain unbiased standard errors of OLS coefficients under a specific kind of heteroscedasticity . Recall that the presence of heteroscedasticity violates the Gauss Markov assumptions that are necessary to render OLS the best linear unbiased estimator…
When to use cluster robust error in inference?
In such settings default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice.
Can a clustered standard error estimator be unbiased?
Furthermore, the covariance structures must be homoskedastic within each cluster. In this case clustered standard errors provide unbiased standard errors estimates. You can find a review on heteroscedasticity and its consequences on the OLS estimator here.
When to use clustered standard errors in OLS?
Clustered Standard Errors. September 25, 2016. Clustered standard errors are a way to obtain unbiased standard errors of OLS coefficients under a specific kind of heteroscedasticity. Recall that the presence of heteroscedasticity violates the Gauss Markov assumptions that are necessary to render OLS the best linear unbiased estimator (BLUE).