Replication Data for: Comparing Dynamic Pies: A Strategy for Modeling Compositional Variables in Time and Space
<|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:'Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationship into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the U.S. states' . 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" : ["Compositional data","Spatial","Time series"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationship into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the U.S. states' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationship into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the U.S. states
Compositional data; Spatial ; Time series