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Should you cluster standard errors when estimating ate at which level?
Using that variance estimator may lead researchers to substantially overreject the null of no treatment effect. Instead, we argue that researchers should cluster their standard errors at the pair level.
Why is it important to use clustered standard errors?
Clustered standard errors are often useful when treatment is assigned at the level of a cluster instead of at the individual level. For example, suppose that an educational researcher wants to discover whether a new teaching technique improves student test scores.
Why are standard errors clustered?
The authors argue that there are two reasons for clustering standard errors: a sampling design reason, which arises because you have sampled data from a population using clustered sampling, and want to say something about the broader population; and an experimental design reason, where the assignment mechanism for some …
Do I need to cluster standard errors?
The general rule is that you still need to cluster if either the sampling or assignment to treatment was clustered. However, the authors show that cluster adjustments will only make an adjustment with fixed effects if there is heterogeneity in treatment effects.
How do clustered standard errors work?
Clustered Standard Errors(CSEs) happen when some observations in a data set are related to each other. This correlation occurs when an individual trait, like ability or socioeconomic background, is identical or similar for groups of observations within clusters.
When to use clustered standard errors in statistics?
To understand when to use clustered standard errors, it helps to take a step back and understand the goal of regression analysis. In statistics, regression models are used to quantify the relationship between one or more predictor variables and a response variable.
When to cluster EHW and LZ standard errors?
Special case: even when the sampling is clustered, the EHW and LZ standard errors will be the same if there is no heterogeneity in the treatment effects.
When do you cluster standard errors by village?
Here you should cluster standard errors by village, since there are villages in the population of interest beyond those seen in the sample.
When do you need to make a cluster adjustment?
The general rule is that you still need to cluster if either the sampling or assignment to treatment was clustered. However, the authors show that cluster adjustments will only make an adjustment with fixed effects if there is heterogeneity in treatment effects.