Robust Inference for Panel Quantile Regression Models with Individual Fixed Effects and Serial Correlation

2016 ◽  
Author(s):  
Jungmo Yoon ◽  
Antonio F Galvao
Keyword(s):  
Quantile Regression ◽  
Fixed Effects ◽  
Regression Models ◽  
Serial Correlation ◽  
Robust Inference ◽  
Panel Quantile Regression

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 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 Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects

10.3982/qe802 ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 579-608
Author(s):  
Jungmo Yoon ◽  
Antonio F. Galvao
Keyword(s):  
Quantile Regression ◽  
Covariance Matrix ◽  
Fixed Effects ◽  
Asymptotic Properties ◽  
Bandwidth Selection ◽  
Temporal Correlation ◽  
Correction Method ◽  
Matrix Estimation ◽  
Panel Quantile Regression ◽  
Robust Tests

This study develops cluster robust inference methods for panel quantile regression (QR) models with individual fixed effects, allowing for temporal correlation within each individual. The conventional QR standard errors can seriously underestimate the uncertainty of estimators and, therefore, overestimate the significance of effects, when outcomes are serially correlated. Thus, we propose a clustered covariance matrix (CCM) estimator to solve this problem. The CCM estimator is an extension of the heteroskedasticity and autocorrelation consistent covariance matrix estimator for QR models with fixed effects. The autocovariance element in the CCM estimator can be substantially biased, due to the incidental parameter problem. Thus, we develop a bias‐correction method for the CCM estimator. We derive an optimal bandwidth formula that minimizes the asymptotic mean squared errors, and propose a data‐driven bandwidth selection rule. We also propose two cluster robust tests, and establish their asymptotic properties. We then illustrate the practical usefulness of the proposed methods using an empirical application.

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