Replication Data for: Modeling Related Processes with an Excess of Zeros
<|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:'Political science research frequently models binary or ordered outcomes involving related processes. However, traditional modeling of these outcomes ignorescommon data issues and cannot capture nuances. There is often an excess ofzeros, the observed outcomes for different actors are inherently related, and competing actors may respond to the same factors differently. This paper extendsexisting models and develops a zero-inflated multivariate ordered probit to simultaneously address these issues. This model performs better than existing modelsat capturing the true parameters of interest, estimates the nature of the relatedprocesses, and captures the differences in actors’ decision-making. I demonstratethese benefits through simulation exercises and an application to party behaviorin Mexico' . 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" : ["Bayesian","Party Competition","Related Processes"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Political science research frequently models binary or ordered outcomes involving related processes. However, traditional modeling of these outcomes ignorescommon data issues and cannot capture nuances. There is often an excess ofzeros, the observed outcomes for different actors are inherently related, and competing actors may respond to the same factors differently. This paper extendsexisting models and develops a zero-inflated multivariate ordered probit to simultaneously address these issues. This model performs better than existing modelsat capturing the true parameters of interest, estimates the nature of the relatedprocesses, and captures the differences in actors’ decision-making. I demonstratethese benefits through simulation exercises and an application to party behaviorin Mexico' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Political science research frequently models binary or ordered outcomes involving related processes. However, traditional modeling of these outcomes ignorescommon data issues and cannot capture nuances. There is often an excess ofzeros, the observed outcomes for different actors are inherently related, and competing actors may respond to the same factors differently. This paper extendsexisting models and develops a zero-inflated multivariate ordered probit to simultaneously address these issues. This model performs better than existing modelsat capturing the true parameters of interest, estimates the nature of the relatedprocesses, and captures the differences in actors’ decision-making. I demonstratethese benefits through simulation exercises and an application to party behaviorin Mexico
Bayesian; Party Competition; Related Processes