Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
This 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 theoretic...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | Perú |
| Institución: | Pontificia Universidad Católica del Perú |
| Repositorio: | PUCP-Institucional |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.pucp.edu.pe:20.500.14657/186814 |
| Acceso en línea: | https://revistas.pucp.edu.pe/index.php/economia/article/view/24201/23459 https://doi.org/10.18800/economia.202102.004 |
| Access Level: | acceso abierto |
| Palabra clave: | Quantile regression Unconditional quantile regression Influence functions https://purl.org/pe-repo/ocde/ford#5.02.01 |
| Sumario: | This 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. |
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