Replication Data for: Treating Time With All Due Seriousness
<|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:'Time series techniques see widespread use in political science. In De Boef and Keele (2008) we outlined a set of statistical methods for stationary data. Those methods have come to be widely used. Grant and Lebo (2015) contend that one of the methods we discussed, the error correction model, should generally not be used with political data. They argue that the error correction model leads to both interpretational and inferential mistakes by applied analysts. While we agree with their statements about equation balance, we show that the error correction model leads to the same inferences as the autoregressive distributed lag model when the data are stationary. We also demonstrate that careful use of an error correction model can help diagnose model misspecification when the equation is unbalanced. Such techniques are useful since pretestin' . 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" : ["Error Correction Models","Time Series Analysis"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Time series techniques see widespread use in political science. In De Boef and Keele (2008) we outlined a set of statistical methods for stationary data. Those methods have come to be widely used. Grant and Lebo (2015) contend that one of the methods we discussed, the error correction model, should generally not be used with political data. They argue that the error correction model leads to both interpretational and inferential mistakes by applied analysts. While we agree with their statements about equation balance, we show that the error correction model leads to the same inferences as the autoregressive distributed lag model when the data are stationary. We also demonstrate that careful use of an error correction model can help diagnose model misspecification when the equation is unbalanced. Such techniques are useful since pretestin' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Time series techniques see widespread use in political science. In De Boef and Keele (2008) we outlined a set of statistical methods for stationary data. Those methods have come to be widely used. Grant and Lebo (2015) contend that one of the methods we discussed, the error correction model, should generally not be used with political data. They argue that the error correction model leads to both interpretational and inferential mistakes by applied analysts. While we agree with their statements about equation balance, we show that the error correction model leads to the same inferences as the autoregressive distributed lag model when the data are stationary. We also demonstrate that careful use of an error correction model can help diagnose model misspecification when the equation is unbalanced. Such techniques are useful since pretestin
Error Correction Models; Time Series Analysis