scholarly journals Improving estimations in quantile regression model with autoregressive errors

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 97-107 ◽  
Author(s):  
Bahadır Yuzbasi ◽  
Yasin Asar ◽  
Samil Sik ◽  
Ahmet Demiralp
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Simulation Experiment ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
White Noise Process ◽  
Respiratory Mortality ◽  
Explanatory Variables ◽  
Quantile Regression Model ◽  
Regression Approach

An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.

scholarly journals Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Philip Kostov
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Rate Of Convergence ◽  
Spatial Dependence ◽  
Spatial Models ◽  
Difficult Problem ◽  
Weighting Matrix ◽  
Quantile Regression Model ◽  
Regression Approach ◽  
The Right

This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model. This selection is a notoriously difficult problem even in linear spatial models and is even more difficult in a quantile regression setup. The proposal is illustrated by an empirical example and manages to produce tractable models. One important feature of the proposed methodology is that by allowing different degrees and forms of spatial dependence across quantiles it further relaxes the usual quantile restriction attributable to the linear quantile regression. In this way we can obtain a more robust, with regard to potential functional misspecification, model, but nevertheless preserve the parametric rate of convergence and the established inferential apparatus associated with the linear quantile regression approach.

scholarly journals Using Quantile Regression to Estimate Intervention Effects Beyond the Mean

2019 ◽  
Vol 79 (5) ◽  
pp. 883-910 ◽  
Author(s):  
Spyros Konstantopoulos ◽  
Wei Li ◽  
Shazia Miller ◽  
Arie van der Ploeg
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Large Scale ◽  
Social Science Research ◽  
Empirical Work ◽  
Science Research ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Intervention Effects ◽  
Education Data

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails).

scholarly journals The impact of AAOIFI governance disclosure on Islamic banks performance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tawida Elgattani ◽  
Khaled Hussainey
Keyword(s):  
Corporate Governance ◽  
Regression Model ◽  
Financial Institution ◽  
Ordinary Least Squares ◽  
Annual Reports ◽  
Islamic Banks ◽  
Least Squares Regression ◽  
Content Type ◽  
The Impact ◽  
Governance Disclosure

Purpose This study aims to investigate the impact of the accounting and auditing organisation for Islamic financial institution (AAOIFI) governance disclosure on the performance of Islamic banks (IBs). Design/methodology/approach The ordinary least squares regression model was used to test the impact of AAOIFI governance disclosure on the performance of 126 IBs from 8 countries that mandatorily adopt the AAOIFI standards for three years (2013–2015). In this regression model, return on asset (ROA) and return on equity (ROE) are the dependent variables, while AAOIFI governance disclosure is the independent variable. Corporate governance mechanisms, firm characteristics, year dummy and country dummy are used as control variables. Findings This paper found an insignificant relationship between AAOIFI governance disclosure and IBs performance. Research limitations/implications This study highlighted the implication that the current research may help IBs and encourage them to disclose more information in annual reports, especially those related to AAOIFI governance standards because following good corporate governance leads to good financial performance. The major limitation of the paper is that it is only focussed on two measurements of bank performance – ROA and ROE; it would be good to use other firm performance measures, such as profit margin. Originality/value This study provides new empirical evidence on the impact of AAOIFI governance disclosure on IBs performance.

scholarly journals ASYMPTOTIC THEORY FOR NONLINEAR QUANTILE REGRESSION UNDER WEAK DEPENDENCE

2015 ◽  
Vol 32 (3) ◽  
pp. 686-713 ◽  
Author(s):  
Walter Oberhofer ◽  
Harry Haupt
Keyword(s):  
Quantile Regression ◽  
Asymptotic Normality ◽  
Regression Model ◽  
Asymptotic Theory ◽  
Asymptotic Properties ◽  
Covariance Estimation ◽  
First Order ◽  
Regression Quantiles ◽  
Quantile Regression Model ◽  
Consistency And Asymptotic Normality

This paper studies the asymptotic properties of the nonlinear quantile regression model under general assumptions on the error process, which is allowed to be heterogeneous and mixing. We derive the consistency and asymptotic normality of regression quantiles under mild assumptions. First-order asymptotic theory is completed by a discussion of consistent covariance estimation.

Sign in / Sign up

Export Citation Format

Share Document