Replication Data for: Regression Discontinuity with Multiple Running Variables Allowing Partial Effects
<|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:'In regression discontinuity (RD), a running variable (or score) crossing a cutoff de-termines a treatment that affects the mean regression function. This paper generalizes this usual one-score mean RD three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This generalization is motivated by (i) many multiple-score RD cases, (ii) the full effect identication needing the partial effects to be separated, and (iii) informative quantile/mode regression functions. We establish identication for multiple-score RD (MRD), and propose simple estimators that become local difference in differences (DD) in case of double scores. We also provide an empirical illus' . 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" : ["regression discontinuity, multiple running variables, partial effect, difference in differences"]} <|eot_id|>
 Here is the Description of a dataset. Description:'In regression discontinuity (RD), a running variable (or score) crossing a cutoff de-termines a treatment that affects the mean regression function. This paper generalizes this usual one-score mean RD three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This generalization is motivated by (i) many multiple-score RD cases, (ii) the full effect identication needing the partial effects to be separated, and (iii) informative quantile/mode regression functions. We establish identication for multiple-score RD (MRD), and propose simple estimators that become local difference in differences (DD) in case of double scores. We also provide an empirical illus' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
In regression discontinuity (RD), a running variable (or score) crossing a cutoff de-termines a treatment that affects the mean regression function. This paper generalizes this usual one-score mean RD three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This generalization is motivated by (i) many multiple-score RD cases, (ii) the full effect identication needing the partial effects to be separated, and (iii) informative quantile/mode regression functions. We establish identication for multiple-score RD (MRD), and propose simple estimators that become local difference in differences (DD) in case of double scores. We also provide an empirical illus
regression discontinuity, multiple running variables, partial effect, difference in differences