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).

Quantile Regression Analysis and Other Alternatives to Ordinary Least Squares Regression

Methodology ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 81-91 ◽  
Author(s):  
Harry Haupt ◽  
Friedrich Lösel ◽  
Mark Stemmler
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Least Squares ◽  
Corporal Punishment ◽  
Ordinary Least Squares ◽  
Study Data ◽  
Physical Punishment ◽  
Least Squares Regression ◽  
Prevention Project ◽  
Quantile Regression Analysis

Data analyses by classical ordinary least squares (OLS) regression techniques often employ unrealistic assumptions, fail to recognize the source and nature of heterogeneity, and are vulnerable to extreme observations. Therefore, this article compares robust and non-robust M-estimator regressions in a statistical demonstration study. Data from the Erlangen-Nuremberg Development and Prevention Project are used to model risk factors for physical punishment by fathers of 485 elementary school children. The Corporal Punishment Scale of the Alabama Parenting Questionnaire was the dependent variable. Fathers’ aggressiveness, dysfunctional parent-child relations, various other parenting characteristics, and socio-demographic variables served as predictors. Robustness diagnostics suggested the use of trimming procedures and outlier diagnostics suggested the use of robust estimators as an alternative to OLS. However, a quantile regression analysis provided more detailed insights beyond the measures of central tendency and detected sources of considerable heterogeneity in the risk structure of father’s corporal punishment. Advantages of this method are discussed with regard to methodological and content issues.

scholarly journals Insights from analysing tourist expenditure using quantile regression

2016 ◽  
Vol 23 (5) ◽  
pp. 1138-1145 ◽  
Author(s):  
António Almeida ◽  
Brian Garrod
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Empirical Studies ◽  
Ordinary Least Squares ◽  
Estimation Technique ◽  
Least Squares Regression ◽  
Data Set ◽  
Detailed Understanding ◽  
Tourism Destinations ◽  
Single Data

Mature tourism destinations are increasingly needing to diversify their products and markets. To be successful, such strategies require a very detailed understanding of potential tourists’ levels and patterns of spending. Empirical studies of tourist expenditure have tended to employ ordinary least squares regression for this purpose. There are, however, a number of important limitations to this technique, chief among which is its inability to distinguish between tourists who have higher- and lower-than-average levels of spending. As such, some researchers recommend the use of an alternative estimation technique, known as quantile regression, which does allow such distinctions to be made. This study uses a single data set, collected among rural tourists in Madeira, to analyse the determinants of tourist expenditure using both techniques. This enables direct comparison to be made and illustrates the additional insights to be gained using quantile regression.

QUANTILE REGRESSION AS A TOOL FOR PORTFOLIO INVESTMENT DECISIONS DURING TIMES OF FINANCIAL DISTRESS

2011 ◽  
Vol 06 (01) ◽  
pp. 1150003 ◽  
Author(s):  
D. E. ALLEN ◽  
R. J. POWELL ◽  
A. K. SINGH
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Financial Distress ◽  
Global Financial Crisis ◽  
Factor Model ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Financial Return ◽  
Investment Tool ◽  
The Global Financial Crisis

The worldwide impact of the Global Financial Crisis (GFC) on stock markets, investors and fund managers has lead to a renewed interest in appropriate tools for robust risk management. Quantile regression is a powerful technique and deserves the interest of financial decision makers given its remarkable capabilities for capturing and explaining the behavior of financial return series across a distribution more effectively than ordinary least squares regression methods which are the standard tool. In this paper, we present quantile regression estimation as an attractive additional investment tool, which is more effective than Ordinary Least Squares (OLS) in analyzing information across the quantiles of a distribution. This translates into the more accurate calibration of asset pricing models and subsequent informational gains in portfolio formation. We present empirical evidence of the superior capabilities of quantile regression based techniques as applied across the quantiles of return distributions to derive information for portfolio formation. We show, via stocks in Dow Jones Industrial Index, that at times of financial shocks, such as the GFC, a portfolio of stocks formed using quantile regression in the context of the Fama–French three-factor model, performs better than the one formed using traditional OLS.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

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.

Using a Probabilistic Model to Assist Merging of Large-Scale Administrative Records

2019 ◽  
Vol 113 (2) ◽  
pp. 353-371 ◽  
Author(s):  
TED ENAMORADO ◽  
BENJAMIN FIFIELD ◽  
KOSUKE IMAI
Keyword(s):  
Large Scale ◽  
Social Science Research ◽  
Auxiliary Information ◽  
Science Research ◽  
Unique Identifier ◽  
Scalable Algorithm ◽  
Administrative Records ◽  
Inaccurate Information ◽  
Multiple Data ◽  
Open Source Software Package

Since most social science research relies on multiple data sources, merging data sets is an essential part of researchers’ workflow. Unfortunately, a unique identifier that unambiguously links records is often unavailable, and data may contain missing and inaccurate information. These problems are severe especially when merging large-scale administrative records. We develop a fast and scalable algorithm to implement a canonical model of probabilistic record linkage that has many advantages over deterministic methods frequently used by social scientists. The proposed methodology efficiently handles millions of observations while accounting for missing data and measurement error, incorporating auxiliary information, and adjusting for uncertainty about merging in post-merge analyses. We conduct comprehensive simulation studies to evaluate the performance of our algorithm in realistic scenarios. We also apply our methodology to merging campaign contribution records, survey data, and nationwide voter files. An open-source software package is available for implementing the proposed methodology.

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