Replication Data for: 'Estimating Substantive Effects in Binary Outcome Panel Models: A Comparison'
<|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:'Dummy variable maximum likelihood (ML) estimation for binary response panel models struggles to estimate coefficients or substantive quantities in either short or rare event panels. The standard response is a conditional ML that  consistently estimates the coefficients on time-varying covariates but  makes substantive effects impossible to compute. In light of this problem, multiple suggestions have appeared for computing these effects, but there is little-to-no guidance as  to when one solution may be preferred to another. I address this question by comparing one of these approaches, a correlated random effects (CRE) estimator, to the maximum likelihood dummy variable estimator (MLDV). I find that when the number within-group observations is small or events are rare, the CRE  is preferred, but as panels get longer the differences betwee' . 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" : ["conditional logit","correlated random effects, fixed effects logit","marginal effects","Panel data"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Dummy variable maximum likelihood (ML) estimation for binary response panel models struggles to estimate coefficients or substantive quantities in either short or rare event panels. The standard response is a conditional ML that  consistently estimates the coefficients on time-varying covariates but  makes substantive effects impossible to compute. In light of this problem, multiple suggestions have appeared for computing these effects, but there is little-to-no guidance as  to when one solution may be preferred to another. I address this question by comparing one of these approaches, a correlated random effects (CRE) estimator, to the maximum likelihood dummy variable estimator (MLDV). I find that when the number within-group observations is small or events are rare, the CRE  is preferred, but as panels get longer the differences betwee' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Dummy variable maximum likelihood (ML) estimation for binary response panel models struggles to estimate coefficients or substantive quantities in either short or rare event panels. The standard response is a conditional ML that  consistently estimates the coefficients on time-varying covariates but  makes substantive effects impossible to compute. In light of this problem, multiple suggestions have appeared for computing these effects, but there is little-to-no guidance as  to when one solution may be preferred to another. I address this question by comparing one of these approaches, a correlated random effects (CRE) estimator, to the maximum likelihood dummy variable estimator (MLDV). I find that when the number within-group observations is small or events are rare, the CRE  is preferred, but as panels get longer the differences betwee
conditional logit; correlated random effects, fixed effects logit; marginal effects; Panel data