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Can a regression with robust error be the same as OLS?
As with the regression with robust error, the estimate of the coefficients are the same as the OLS estimates, but the standard errors take into account that the observations within districts are non-independent.
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.
Do you need a model for within cluster error correlation?
These cluster-robust standard errors do not require specification of a model for within-cluster error correlation, but do require the additional assumption that the number of clusters, rather than just the number of observations, goes to infinity.
Which is an example of a clustered error?
One leading example of “clustered errors” is in dividual-level cross-section data with clustering on geographical region, such as village or state. Then model errors for individuals in the same region may be correlated, while model errors for individuals in different regions are assumed to be uncorrelat ed.
How are the residuals independent in OLS regression?
As described in Chapter 2, OLS regression assumes that the residuals are independent. The elemapi2 dataset contains data on 400 schools that come from 37 school districts. It is very possible that the scores within each school district may not be independent, and this could lead to residuals that are not independent within districts.
How is the SUR method similar to OLS?
Like OLS, the SUR method assumes that all the regressors are independent variables, but SUR uses the correlations among the errors in different equations to improve the regression estimates. The SUR method requires an initial OLS regression to compute residuals.
How is a regression plot different from an OLS plot?
Here, of course, is the graph of residuals versus fitted (predicted) with a line at zero. This plot looks much like the OLS plot, except that in the OLS all of the observations would be weighted equally, but as we saw above the observations with the greatest residuals are weighted less and hence have less influence on the results.