Clustered Std Errs vs Unclustered

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SUMMARY

The discussion centers on the comparison between clustered and unclustered standard errors in regression analysis, specifically when clustering around states with fixed time effects. The user observed that clustered standard errors were approximately twice as large as those from the unclustered regression. This discrepancy is attributed to the potential effects of serial correlation in the data. The conversation highlights the importance of specifying the clustering technique used for accurate interpretation of results.

PREREQUISITES
  • Understanding of regression analysis and fixed effects models.
  • Familiarity with the concept of clustering standard errors in econometrics.
  • Knowledge of serial correlation and its implications in statistical modeling.
  • Experience with statistical software capable of running regressions, such as R or Stata.
NEXT STEPS
  • Research the impact of serial correlation on regression results and standard errors.
  • Learn about different clustering techniques and their appropriate applications in regression analysis.
  • Explore how to implement clustered standard errors in R using the 'sandwich' package.
  • Investigate the implications of fixed effects in the context of panel data analysis.
USEFUL FOR

Economists, data analysts, and researchers conducting regression analysis who need to understand the implications of clustered versus unclustered standard errors in their models.

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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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.
 

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