scholarly journals Finite Sample Inference for Quantile Regression Models

2006 ◽  
Author(s):  
Victor Chernozhukov ◽  
Christian Hansen ◽  
Michael Jansson
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Finite Sample ◽  
Finite Sample Inference

scholarly journals Finite sample inference for quantile regression models

2009 ◽  
Vol 152 (2) ◽  
pp. 93-103 ◽  
Author(s):  
Victor Chernozhukov ◽  
Christian Hansen ◽  
Michael Jansson
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Finite Sample ◽  
Finite Sample Inference

A Test for Functional Form Against Nonparametric Alternatives

1992 ◽  
Vol 8 (4) ◽  
pp. 452-475 ◽  
Author(s):  
Jeffrey M. Wooldridge
Keyword(s):  
Alternative Model ◽  
Regression Models ◽  
Functional Form ◽  
Parametric Model ◽  
Finite Sample ◽  
Test Statistic ◽  
Sieve Estimation ◽  
Finite Sample Properties ◽  
Standard Normal ◽  
Nonparametric Alternatives

A test for neglected nonlinearities in regression models is proposed. The test is of the Davidson-MacKinnon type against an increasingly rich set of non-nested alternatives, and is based on sieve estimation of the alternative model. For the case of a linear parametric model, the test statistic is shown to be asymptotically standard normal under the null, while rejecting with probability going to one if the linear model is misspecified. A small simulation study suggests that the test has adequate finite sample properties, but one must guard against over fitting the nonparametric alternative.

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.

scholarly journals The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akram Yazdani ◽  
Mehdi Yaseri ◽  
Shahpar Haghighat ◽  
Ahmad Kaviani ◽  
Hojjat Zeraati
Keyword(s):  
Breast Cancer ◽  
Quantile Regression ◽  
Survival Time ◽  
Censored Data ◽  
Regression Models ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Right Censored Data ◽  
Censored Quantile Regression ◽  
Covariate Effects

AbstractThe Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

scholarly journals Adaptively weighted group Lasso for semiparametric quantile regression models

Bernoulli ◽  
2019 ◽  
Vol 25 (4B) ◽  
pp. 3311-3338
Author(s):  
Toshio Honda ◽  
Ching-Kang Ing ◽  
Wei-Ying Wu
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Group Lasso
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