dc.creatorAlejo, Javier
dc.creatorFavata, Federico
dc.creatorMontes-Rojas, Gabriel
dc.creatorTrombetta, Martín
dc.date2021-12-31
dc.date.accessioned2023-03-08T19:00:23Z
dc.date.available2023-03-08T19:00:23Z
dc.identifierhttps://revistas.pucp.edu.pe/index.php/economia/article/view/24201
dc.identifier10.18800/economia.202102.004
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5961774
dc.descriptionThis paper analyzes two econometric tools that are used to evaluate distributional effects, conditional quantile regression (CQR) and unconditional quantile regression (UQR). Our main objective is to shed light on the similarities and differences between these methodologies. An interesting theoretical derivation to connect CQR and UQR is that, for the effect of a continuous covariate, the UQR is a weighted average of the CQR. This imposes clear bounds on the values that UQR coefficients can take and provides a way to detect misspecification. The key here is a match between CQR whose predicted values are the closest to the unconditional quantile. For a binary covariate, however, we derive a new analytical relationship. We illustrate these models using age returns and gender gap in Argentina for 2019 and 2020.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherPontificia Universidad Católica del Perúen-US
dc.relationhttps://revistas.pucp.edu.pe/index.php/economia/article/view/24201/23459
dc.rightsDerechos de autor 2021 Gabriel Montes-Rojas, Javier Alejo, Federico Favata, Martín Trombettaes-ES
dc.rightshttp://creativecommons.org/licenses/by/4.0es-ES
dc.sourceEconomía; Volume 44 Issue 88 (2021); 76-93es-ES
dc.source2304-4306
dc.source0254-4415
dc.subjectQuantile regressionen-US
dc.subjectUnconditional quantile regressionen-US
dc.subjectInfluence functionsen-US
dc.titleConditional vs Unconditional Quantile Regression Models: A Guide to Practitionersen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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