Replication Data for: Corrected Standard Errors with Clustered Data
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The use of cluster robust standard errors (CRSE) is commonas data are often collected from units, such as cities, states or countries, with multiple observations per unit.  There is considerable discussion of how best to estimate standard errors and confidence intervals when using CRSE.  Extensive simulations in this literature and here show that CRSE seriously underestimate coefficient standard errors and their associated confidence intervals, particularly with a small number of clusters and when there is little within cluster variation in the explanatory variables.  These same simulations show that a method developed here provides more reliable estimates of coefficient standard errors.  They underestimate confidence intervals for tests of individual and sets of coefficients in extreme conditions, but by far less than do CRSE.  Simulat' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["clustered standard errors"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The use of cluster robust standard errors (CRSE) is commonas data are often collected from units, such as cities, states or countries, with multiple observations per unit.  There is considerable discussion of how best to estimate standard errors and confidence intervals when using CRSE.  Extensive simulations in this literature and here show that CRSE seriously underestimate coefficient standard errors and their associated confidence intervals, particularly with a small number of clusters and when there is little within cluster variation in the explanatory variables.  These same simulations show that a method developed here provides more reliable estimates of coefficient standard errors.  They underestimate confidence intervals for tests of individual and sets of coefficients in extreme conditions, but by far less than do CRSE.  Simulat' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The use of cluster robust standard errors (CRSE) is commonas data are often collected from units, such as cities, states or countries, with multiple observations per unit.  There is considerable discussion of how best to estimate standard errors and confidence intervals when using CRSE.  Extensive simulations in this literature and here show that CRSE seriously underestimate coefficient standard errors and their associated confidence intervals, particularly with a small number of clusters and when there is little within cluster variation in the explanatory variables.  These same simulations show that a method developed here provides more reliable estimates of coefficient standard errors.  They underestimate confidence intervals for tests of individual and sets of coefficients in extreme conditions, but by far less than do CRSE.  Simulat
clustered standard errors