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

scholarly journals Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models

2018 ◽  
Vol 114 (526) ◽  
pp. 749-758 ◽  
Author(s):  
Alexandre Belloni ◽  
Victor Chernozhukov ◽  
Kengo Kato
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
High Dimensional

Lack-of-fit tests for quantile regression models

2019 ◽  
Vol 81 (3) ◽  
pp. 629-648 ◽  
Author(s):  
Chen Dong ◽  
Guodong Li ◽  
Xingdong Feng
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Lack Of Fit ◽  
Lack Of Fit Tests

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.

Weak Signals in High‐Dimensional Poisson Regression Models

2021 ◽  
pp. 203-219
Author(s):  
Orawan Reangsephet ◽  
Supranee Lisawadi ◽  
Syed Ejaz Ahmed
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
High Dimensional ◽  
Weak Signals

scholarly journals Forecasting Large Price Declines of the Nikkei Using the S&P 500 Implied Volatility

2016 ◽  
Vol 8 (1) ◽  
pp. 58
Author(s):  
Chikashi Tsuji
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Implied Volatility ◽  
Predictive Power ◽  
Downside Risk ◽  
Price Changes ◽  
Empirical Results ◽  
Left Tail

This paper empirically examines the forecast power of the previous day’s US implied volatility for large declines of the Nikkei by using several versions of quantile regression models. All our empirical results suggest that the previous day’s US S&P 500 implied volatility has forecast power for large price drops of the Nikkei 225 in Japan. Since we repeatedly and carefully tested the several left tail risks in price changes of the Nikkei and we also tested by using some different versions of quantile regression models, our evidence of the predictive power of the S&P 500 implied volatility for downside risk of the Nikkei is very robust.

scholarly journals Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuecai Xu ◽  
Željko Šarić
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Close Relation ◽  
Warning Signs ◽  
Material Damage ◽  
Accident Rate ◽  
Traffic Signs ◽  
Accident Severity ◽  
Severity Levels ◽  
The Republic

This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; (2) average speed limit exhibited a close relation with accident rate; and (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury.

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