Replication Data for: Transformed-Likelihood Estimators for Dynamic Panel Models with a Very Small T
<|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:'Conventional OLS fixed-effects and GLS random-effects estimators of dynamic modelsthat control for individual-effects are known to be biased when applied to shortpanel data (T <= 10). GMM estimators are the most used alternative but are known tohave drawbacks. Transformed-likelihood estimators are unused in political science.Of these, orthogonal reparameterization estimators are only tangentially referred to inany discipline. We introduce these estimators and test their performance, demonstratingthat the unused orthogonal reparameterization transformed-likelihood estimatorin particular performs very well and is an improvement on the commonly used GMMestimators. When T and/or N are small, it provides efficiency gains and overcomesthe issues GMM estimators encounter in the estimation of long-run effects when thecoeffic' . 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" : ["panel data","dynamic","unobserved heterogeneity","individual effects","transformed likelihood","Bayesian"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Conventional OLS fixed-effects and GLS random-effects estimators of dynamic modelsthat control for individual-effects are known to be biased when applied to shortpanel data (T <= 10). GMM estimators are the most used alternative but are known tohave drawbacks. Transformed-likelihood estimators are unused in political science.Of these, orthogonal reparameterization estimators are only tangentially referred to inany discipline. We introduce these estimators and test their performance, demonstratingthat the unused orthogonal reparameterization transformed-likelihood estimatorin particular performs very well and is an improvement on the commonly used GMMestimators. When T and/or N are small, it provides efficiency gains and overcomesthe issues GMM estimators encounter in the estimation of long-run effects when thecoeffic' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Conventional OLS fixed-effects and GLS random-effects estimators of dynamic modelsthat control for individual-effects are known to be biased when applied to shortpanel data (T <= 10). GMM estimators are the most used alternative but are known tohave drawbacks. Transformed-likelihood estimators are unused in political science.Of these, orthogonal reparameterization estimators are only tangentially referred to inany discipline. We introduce these estimators and test their performance, demonstratingthat the unused orthogonal reparameterization transformed-likelihood estimatorin particular performs very well and is an improvement on the commonly used GMMestimators. When T and/or N are small, it provides efficiency gains and overcomesthe issues GMM estimators encounter in the estimation of long-run effects when thecoeffic
panel data; dynamic; unobserved heterogeneity; individual effects; transformed likelihood; Bayesian