What is clustered Standard Error approach ? Why standard errors need to be corrected in this scenario?
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Clustered standard error approach is a statistical method used in regression analysis to account for correlation or dependence among observations within a cluster or group. In many real-world settings, observations within a group tend to be more similar to each other than to observations in other groups, and this can lead to the violation of the assumption of independent errors in a regression model.
To address this issue, the clustered standard error approach estimates the standard errors of regression coefficients while taking into account the correlation or dependence of observations within a cluster or group. The approach works by clustering the observations into groups and calculating the standard error of the regression coefficients separately for each cluster.
By doing so, the approach accounts for the correlation within each cluster, while assuming independence across clusters. The result is a more accurate estimation of the standard errors of regression coefficients, which in turn improves the accuracy of hypothesis testing and confidence interval construction.
Clustered standard errors are commonly used in economics, finance, and other fields that analyze data collected from clusters or groups, such as households, firms, schools, or geographic regions.