Replication Data for: Lagged outcomes, lagged predictors, and lagged errors: A clarification on common factors
<|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:'Debate on the use of lagged dependent variables has a long history in political science. The latest contribution to this discussion is Wilkins (2018), which advocates the use of an ADL(2,1) model when there is serial dependence in the outcome and disturbance. While this specification does offer some insurance against serially correlated disturbances, this is never the best (linear unbiased estimator) approach and should not be pursued as a general strategy. First, this strategy is only appropriate when the data-generating process (DGP) actually implies a more parsimonious model. Second, when this is :emph{not} the DGP -- ex. lags of the predictors have independent effects -- this strategy mischaracterizes the dynamic process. We clarify this issue and detail a Wald test that can be used to evaluate the appropriateness of the Wilkins (20' . 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" : ["Lagged Dependent Variables","Specification Testing","Time Series"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Debate on the use of lagged dependent variables has a long history in political science. The latest contribution to this discussion is Wilkins (2018), which advocates the use of an ADL(2,1) model when there is serial dependence in the outcome and disturbance. While this specification does offer some insurance against serially correlated disturbances, this is never the best (linear unbiased estimator) approach and should not be pursued as a general strategy. First, this strategy is only appropriate when the data-generating process (DGP) actually implies a more parsimonious model. Second, when this is :emph{not} the DGP -- ex. lags of the predictors have independent effects -- this strategy mischaracterizes the dynamic process. We clarify this issue and detail a Wald test that can be used to evaluate the appropriateness of the Wilkins (20' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Debate on the use of lagged dependent variables has a long history in political science. The latest contribution to this discussion is Wilkins (2018), which advocates the use of an ADL(2,1) model when there is serial dependence in the outcome and disturbance. While this specification does offer some insurance against serially correlated disturbances, this is never the best (linear unbiased estimator) approach and should not be pursued as a general strategy. First, this strategy is only appropriate when the data-generating process (DGP) actually implies a more parsimonious model. Second, when this is :emph{not} the DGP -- ex. lags of the predictors have independent effects -- this strategy mischaracterizes the dynamic process. We clarify this issue and detail a Wald test that can be used to evaluate the appropriateness of the Wilkins (20
Lagged Dependent Variables; Specification Testing; Time Series