scholarly journals Flexible and fast estimation of quantile treatment effects: The rqr and rqrplot commands

2021 ◽  
Author(s):  
Nicolai T. Borgen ◽  
Andreas Haupt ◽  
Øyvind N. Wiborg
Keyword(s):  
Quantile Regression ◽  
Fixed Effects ◽  
Regression Models ◽  
Treatment Effects ◽  
The Other ◽  
High Dimensional ◽  
Fast Estimation ◽  
Quantile Treatment Effects

Using quantile regression models to estimate quantile treatment effects is becoming increasingly popular. This paper introduces the rqr command that can be used to estimate residualized quantile regression (RQR) coefficients and the rqrplot postestimation command that can be used to effortless plot the coefficients. The main advantages of the rqr command compared to other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.

scholarly journals A New Framework for Estimation of Unconditional Quantile Treatment Effects: The Residualized Quantile Regression (RQR) Model

2021 ◽  
Author(s):  
Nicolai T. Borgen ◽  
Andreas Haupt ◽  
Øyvind N. Wiborg
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Fixed Effects ◽  
Treatment Effects ◽  
Continuous Treatment ◽  
High Dimensional ◽  
Treatment Variable ◽  
Quantile Regression Model ◽  
Quantile Treatment Effects ◽  
New Framework

The identification of unconditional quantile treatment effects (QTE) has become increasingly popular within social sciences. However, current methods to identify unconditional QTEs of continuous treatment variables are incomplete. Contrary to popular belief, the unconditional quantile regression model introduced by Firpo, Fortin, and Lemieux (2009) does not identify QTE, while the propensity score framework of Firpo (2007) allows for only a binary treatment variable, and the generalized quantile regression model of Powell (2020) is unfeasible with high-dimensional fixed effects. This paper introduces a two-step approach to estimate unconditional QTEs where the treatment variable is first regressed on the control variables followed by a quantile regression of the outcome on the residualized treatment variable. Unlike much of the literature on quantile regression, this two-step residualized quantile regression framework is easy to understand, computationally fast, and can include high-dimensional fixed effects.

scholarly journals Quantile Regression Models and the Crucial Difference Between Individual-Level Effects and Population-Level Effects

2020 ◽  
Author(s):  
Nicolai T. Borgen ◽  
Andreas Haupt ◽  
Øyvind N. Wiborg
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Treatment Effects ◽  
Population Level ◽  
Conditional Quantile ◽  
Individual Level ◽  
Quantile Treatment Effects ◽  
Crucial Difference ◽  
Practical Guidelines ◽  
Conceptual Distinction

The unconditional quantile regression (UQR) model – which has gained increasing popularity in the 2010s and is regularly applied in top-rated academic journals within sociology and other disciplines – is poorly understood and frequently misinterpreted. The main reason for its increased popularity is that the UQR model seemingly tackles an issue with the traditional conditional quantile regression (CQR) model: the interpretation of coefficients as quantile treatment effects changes whenever control variables are included. However, the UQR model was not developed to solve this issue but to study influences on quantile values of the overall outcome distribution. This paper clarifies the crucial conceptual distinction between influences on overall distributions, which we term population-level influences, and individual-level quantile treatment effects. Further, we use data simulations to illustrate that various classes of quantile regression models may, in some instances, give entirely different conclusions (to different questions). The conceptual and empirical distinctions between various quantile regression models underline the need to match the correct quantile regression model to the specific research questions. We conclude the paper with some practical guidelines for researchers.

scholarly journals Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

2020 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Michelle Lee Maroto
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Regression Models ◽  
Wage Distribution ◽  
Motherhood Penalty ◽  
Unconditional Quantile Regression ◽  
Quantile Treatment Effect

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

scholarly journals A lack-of-fit test for quantile regression models with high-dimensional covariates

2015 ◽  
Vol 88 ◽  
pp. 128-138 ◽  
Author(s):  
Mercedes Conde-Amboage ◽  
César Sánchez-Sellero ◽  
Wenceslao González-Manteiga
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
High Dimensional ◽  
Lack Of Fit
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