Replication Data for: A Diffusion Network Event History Estimator
<|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:'Research on the diffusion of political decisions across jurisdictions typically accounts for units' influence over each other with (1) observable measures and/or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues, but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce Network Event History Analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA's unique methodological and substantive benefits in applications to policy adoption in the American states. Researchers can analyze the ties and structure of inferred networks to refine model specifications, e' . 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" : ["Diffusion","Event history analysis","Network inference","Policy adoption"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Research on the diffusion of political decisions across jurisdictions typically accounts for units' influence over each other with (1) observable measures and/or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues, but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce Network Event History Analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA's unique methodological and substantive benefits in applications to policy adoption in the American states. Researchers can analyze the ties and structure of inferred networks to refine model specifications, e' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Research on the diffusion of political decisions across jurisdictions typically accounts for units' influence over each other with (1) observable measures and/or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues, but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce Network Event History Analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA's unique methodological and substantive benefits in applications to policy adoption in the American states. Researchers can analyze the ties and structure of inferred networks to refine model specifications, e
Diffusion; Event history analysis; Network inference; Policy adoption