Clustered Std Errs vs Unclustered

  • Thread starter TheBestMilke
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In summary, the individual has run two regressions, one with clustering around states from their data and one without clustering. Both regressions have fixed time effects. However, the standard errors for the main coefficients in the clustered regression are about twice the size of the standard errors in the unclustered regression. The individual is wondering if this difference could be due to serial correlation and is looking for clarification on the specific clustering technique used and the presence of a time stamp in the data.
  • #1
TheBestMilke
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Greetings,

I recently ran two regressions, one clustering around the states from my data and the other without clustering. Both have fixed time effects. I've noticed that the standard errors for my main coefficients under observation are much larger than the standard errors from my unclustered regression. Is there a reason for this difference (the clustered std errors are about twice as big) and could it be an effect of serial correlation?

Thanks!
 
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  • #2
TheBestMilke said:
Greetings,

I recently ran two regressions, one clustering around the states from my data and the other without clustering. Both have fixed time effects. I've noticed that the standard errors for my main coefficients under observation are much larger than the standard errors from my unclustered regression. Is there a reason for this difference (the clustered std errors are about twice as big) and could it be an effect of serial correlation?

You should have another go at explaining your question. "Clustering" is general idea, but it isn't clear what specific clustering technique you used. Your statement indicates that the data may have a time stamp to it, but not much else.
 

What is the difference between clustered and unclustered standard errors?

Clustered standard errors take into account the correlation between observations within a cluster, whereas unclustered standard errors assume that all observations are independent.

When should I use clustered standard errors?

Clustered standard errors should be used when there is a high likelihood of correlation between observations within a cluster. This is often the case in clustered or hierarchical data structures, such as when observations are grouped by geographic location or by individuals.

What are the advantages of using clustered standard errors?

Clustered standard errors provide more accurate estimates of standard errors when there is correlation within clusters. This can lead to more reliable statistical inference and can reduce the risk of Type I errors.

Are clustered standard errors always necessary?

No, clustered standard errors are not always necessary. They should only be used when there is evidence of correlation within clusters. If there is no correlation, using clustered standard errors can actually lead to less accurate estimates.

How are clustered standard errors calculated?

Clustered standard errors are typically calculated using a robust estimation method, such as the Huber-White sandwich estimator. This method takes into account the correlation within clusters by adjusting the covariance matrix used in calculating the standard errors.

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