- In this brief note, we will review quantile and unconditional quantile models, and propose a new method of Quantile Regression (QR) called Percentile Weights Regression (PWR). It is important to recall that the main objective of these models is to estimate appropriate coefficients for a given percentile, based on the rank of the dependent variable or its predicted part. This is because the impact of a given explanatory variable on the outcome can be heterogeneous across different levels of the outcome. For example, the incidence of a social assistance program can vary depending on the level of wellbeing. QR models are useful in showing such heterogeneity.
Project leader: Abdelkrim Araar
Project researchers:
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| Title | Modified | Size | Comments | Recommendations | |
|---|---|---|---|---|---|
| Exploring Heterogeneous Effects: Quantile Models and Percentile Weights Regression | 2023-10-30 | 1.43MB | 0 | 0 |
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