Relationship of Trace Metal Covariates and pH Distribution in Groundwater within Gold mining and Non-Gold mining Areas in Ghana

One of the most important defining characteristics of groundwater quality is pH as it fundamentally controls the amount and chemical form of many organic and inorganic solutes in groundwater. Groundwater data are frequently characterized by a wide degree of variability of the factors which possibly influence pH distribution. For this reason, it is challenging to link the spatio-temporal dynamics of pH to a single environmental factor by the ordinary least squares regression technique of the conditional mean. In this study, quantile regression was used to estimate the response of pH to nine environmental factors (As, Cd, Fe, Mn, Pb, turbidity, electrical conductivity, total dissolved solids and nitrates). Results of 25%, 50%, 75% quantile regression and ordinary least squares (OLS) regression were compared. The standard regression of the conditional means (OLS) underestimated the rates of change of pH due to the selected factors in comparison with the regression quantiles. The effect of arsenic increased for sampling locations with higher pH values (higher quantiles) likewise the influence of Pb and Mn. However, the effects of Cd and Fe decreased for sampling locations in higher quantiles. It can be concluded that these detected heterogeneities would be missed if this study had focused exclusively on the conditional means of the pH values. Consequently, quantile regression provides a more comprehensive account of possible spatio-temporal relationships between environmental covariates in groundwater. This study is one of the first to apply this technique on groundwater systems in sub-Saharan Africa. The approach is useful and interesting and has broad application for other mining environments especially tropical low-income countries where climatic conditions can drive rapid cycling or transformations of pollutants. It is also pertinent to geopolitical contexts where regulatory; monitoring and management capacities are weak and where mining pollution of groundwater largely occur.

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Using Quantile Regression to Estimate Intervention Effects Beyond the Mean

Educational and Psychological Measurement ◽

10.1177/0013164419837321 ◽

2019 ◽

Vol 79 (5) ◽

pp. 883-910 ◽

Cited By ~ 1

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).

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The Impact of Climate Variability and Change on Food Security in Sub-Saharan Africa: Perspective from Panel Data Analysis

Sustainability ◽

10.3390/su14020759 ◽

2022 ◽

Vol 14 (2) ◽

pp. 759

Author(s):

Raïfatou Affoh ◽

Haixia Zheng ◽

Kokou Dangui ◽

Badoubatoba Mathieu Dissani

Keyword(s):

Food Security ◽

Least Squares ◽

Food Availability ◽

Co2 Emission ◽

Ordinary Least Squares ◽

Causal Link ◽

Sub Saharan Africa ◽

Food Utilization ◽

Sub Saharan ◽

The Impact

This study investigates the relationship between climate variables such as rainfall amount, temperature, and carbon dioxide (CO2) emission and the triple dimension of food security (availability, accessibility, and utilization) in a panel of 25 sub-Saharan African countries from 1985 to 2018. After testing for cross-sectional dependence, unit root and cointegration, the study estimated the pool mean group (PMG) panel autoregressive distributed lag (ARDL). The empirical outcome revealed that rainfall had a significantly positive effect on food availability, accessibility, and utilization in the long run. In contrast, temperature was harmful to food availability and accessibility and had no impact on food utilization. Lastly, CO2 emission positively impacted food availability and accessibility but did not affect food utilization. The study took a step further by integrating some additional variables and performed the panel fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) regression to ensure the robustness of the preceding PMG results. The control variables yielded meaningful results in most cases, so did the FMOLS and DOLS regression. The Granger causality test was conducted to determine the causal link, if any, among the variables. There was evidence of a short-run causal relationship between food availability and CO2 emission. Food accessibility exhibited a causal association with temperature, whereas food utilization was strongly connected with temperature. CO2 emission was linked to rainfall. Lastly, a bidirectional causal link was found between rainfall and temperature. Recommendations to the national, sub-regional, and regional policymakers are addressed and discussed.

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Quantile Regression Analysis and Other Alternatives to Ordinary Least Squares Regression

Methodology ◽

10.1027/1614-2241/a000077 ◽

2014 ◽

Vol 10 (3) ◽

pp. 81-91 ◽

Cited By ~ 3

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.

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Value-added in higher education: ordinary least squares and quantile regression for a Colombian case

Ingeniería e Investigación ◽

10.15446/ing.investig.v37n3.61729 ◽

2017 ◽

Vol 37 (3) ◽

pp. 30-36

Author(s):

Jose D Bogoya ◽

Johan M Bogoya ◽

Alfonso J Peñuela

Keyword(s):

Higher Education ◽

Quantile Regression ◽

Least Squares ◽

Value Added ◽

Ordinary Least Squares ◽

Independent Variables ◽

State Tests ◽

Socioeconomic Stratum ◽

The Mean ◽

Lineal Regression

Colombia applies two mandatory National State tests every year. The first, known as Saber 11, is applied to students who finish the high school cycle, whereas the second, called Saber Pro, is applied to students who finish the higher education cycle. The result obtained by a student on the Saber 11 exam along with his/her gender, and socioeconomic stratum are our independent variables while the Saber Pro outcome is our dependent variable.We compare the results of two statistical models for the Saber Pro exam. The first model, multi-lineal regression or ordinary least squares (OLS), produces an overall well fitted result but is highly inaccurate for some students. The second model, quantile regression (QR), weight the population according to their quantile groups. OLS minimizes the errors for the students whose Saber Pro result is close to the mean (a process known as estimation in the mean) while QR can estimate in the -quantile for every . We show that QR is more accurate than OLS and reveal the unknown behavior of the socioeconomic stratum, the gender, and the initial academic endowments (estimated by the Saber 11 exam) for each quantile group.

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Chinese and Indian Stock Markets: Linkages and Interdependencies

Research in World Economy ◽

10.5430/rwe.v12n2p228 ◽

2021 ◽

Vol 12 (2) ◽

pp. 228

Author(s):

Karthigai Prakasam Chellaswamy ◽

Natchimuthu N. ◽

Muhammadriyaj Faniband

Keyword(s):

Quantile Regression ◽

Least Squares ◽

Ordinary Least Squares ◽

Dependence Structure ◽

Macroeconomic Variables ◽

China And India ◽

Time Period ◽

Regression Approach ◽

Small Change ◽

Recession Period

This paper examines the stock market linkages and interdependencies between China and India. We use the quantile regression approach as an alternative to Ordinary Least Squares estimation due to its flexibleness and robustness. Our results of the entire time period reveal the influence of Chinese CPI and ER on Nifty returns is not the same across the different quantiles. However, Chinese IR has no impact on Nifty returns. Further, Indian CPI has a negligible effect on SSE returns. In contrast, IR and ER do not affect SSE returns. This study also observes that the dependence structure between CPI and SSE returns indicates a negligible change post-recession period. However, the dependence structure between IR, ER, and SSE returns has not changed after the recession. Further, a significantly small change is found in the dependence structure between Chinese macroeconomic variables and Nifty returns post-recession.

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Nonlinear quantile regression to describe the dry matter accumulation of garlic plants

Ciência Rural ◽

10.1590/0103-8478cr20180385 ◽

2020 ◽

Vol 50 (1) ◽

Author(s):

Guilherme Alves Puiatti ◽

Paulo Roberto Cecon ◽

Moysés Nascimento ◽

Ana Carolina Campana Nascimento ◽

Antônio Policarpo Souza Carneiro ◽

...

Keyword(s):

Quantile Regression ◽

Least Squares ◽

Nonlinear Regression ◽

Regression Models ◽

Dry Matter ◽

Goodness Of Fit ◽

Least Squares Method ◽

Ordinary Least Squares ◽

Dry Matter Accumulation ◽

Over Time

ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

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A Unified Approach to Robust, Regression-Based Specification Tests

Econometric Theory ◽

10.1017/s0266466600004898 ◽

1990 ◽

Vol 6 (1) ◽

pp. 17-43 ◽

Cited By ~ 189

Author(s):

Jeffrey M. Wooldridge

Keyword(s):

Least Squares ◽

Robust Regression ◽

Nonlinear Least Squares ◽

Ordinary Least Squares ◽

Conditional Variance ◽

Least Squares Regression ◽

Specification Tests ◽

Conditional Mean ◽

Quasi Maximum Likelihood ◽

Conditional Means

This paper develops a general approach to robust, regression-based specification tests for (possibly) dynamic econometric models. A useful feature of the proposed tests is that, in addition to estimation under the null hypothesis, computation requires only a matrix linear least-squares regression and then an ordinary least-squares regression similar to those employed in popular nonrobust tests. For the leading cases of conditional mean and/or conditional variance tests, the proposed statistics are robust to departures from distributional assumptions that are not being tested, while maintaining asymptotic efficiency under ideal conditions. Moreover, the statistics can be computed using any √T-consistent estimator, resulting in significant simplifications in some otherwise difficult contexts. Among the examples covered are conditional mean tests for models estimated by weighted nonlinear least squares under misspecification of the conditional variance, tests of jointly parameterized conditional means and variances estimated by quasi-maximum likelihood under nonnormality, and some robust specification tests for a dynamic linear model estimated by two-stage least squares.

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Nativity and Wealth in Mid-Nineteenth-Century Cities

The Journal of Economic History ◽

10.1017/s0022050700020581 ◽

1998 ◽

Vol 58 (2) ◽

pp. 468-493 ◽

Cited By ~ 32

Author(s):

Timothy G. Conley ◽

David W. Galenson

Keyword(s):

Nineteenth Century ◽

New York ◽

Quantile Regression ◽

Least Squares ◽

Multivariate Analyses ◽

Ordinary Least Squares ◽

Adult Males

This article uses evidence from the manuscripts of the 1860 federal census to analyze the wealth of adult males in Boston, New York, Chicago, and Indianapolis. Previous multivariate analyses of wealth from the census have been flawed by reliance on ordinary least squares; we instead use quantile regression. Immigrants fared considerably better in the Midwest than the East: immigrants in the midwestern cities held more wealth than their eastern counterparts, both absolutely and relative to the native-born in their respective cities. We explore the causes of these differences and their consequences for nineteenth-century Americans and their communities.

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Acute Performance Estimation of Students Using Quantile Regression Approach (A Case Study of Lahore)

Pakistan Journal of Education ◽

10.30971/pje.v36i3.850 ◽

2020 ◽

Vol 36 (3) ◽

Author(s):

Sunaina Ishtiaq ◽

Yasar Mahmood ◽

Dr. Hina Khan

Keyword(s):

Quantile Regression ◽

Least Squares ◽

Student Performance ◽

High Performance ◽

Positive Impact ◽

Ordinary Least Squares ◽

Performance Estimation ◽

Policy Implications ◽

High Quantiles ◽

Private And Public

Extreme behavior (Performance) of students is inclined by number of factor which must be painted for important policy implications. This study states that the CGPA is the most important system to deduct student performance. Data on CGPA has been collected from B.A/B.Sc (Hons.) of 32 private and public universities of Lahore. Generally, researchers investigate an average performance of the students with classical methods of simple linear regression. This approach does not give complete picture of different variables influencing student performance from corner to corner. Quantile regression introduces information across the whole distribution of the student’s achievements. Study furnishes that students performance strongly affected by father’s education. Student’s gender, passion for fashion, and mother’s job are significant factors. Class participation is found as a magical variable that has positive impact on student performance at all quantiles. The quantile estimate of student performance shows that effect of the urban-rural difference is significant factor. The study clearly shows for high performance students, factors like mother occupation, father education, gender and area become insignificant at high quantiles. The results highlight that quantile regression model is a useful technique for examine information than ordinary least squares. It also depicts that ordinary least squares underestimated and overestimated the Quantile regression at different quantiles.

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Pricing Mechanism and IPO Initial Return: Evidence from Pakistan Stock Exchange

International Journal of Business and Society ◽

10.33736/ijbs.3347.2020 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1239-1257

Author(s):

Waqas Mehmood ◽

Rasidah Mohd-Rashid ◽

Abd Halim Ahmad

Keyword(s):

Quantile Regression ◽

Least Squares ◽

Stepwise Regression ◽

Stock Exchange ◽

Ordinary Least Squares ◽

Cross Sectional ◽

Pricing Mechanism ◽

Fixed Price ◽

Initial Return ◽

Book Building

This study adds to the extent of the literature by examining the impacts of pricing mechanism and premium offered on IPO initial return in Pakistan. Cross-sectional data were gathered using 90 listed IPOs retrieved from Pakistan stock exchange. Accordingly, ordinary least squares, quantile regression, robustness regression, and stepwise regression were employed to assess the factors that influenced initial return. This study describes the intensity of initial return in light of company specific and issue specific variables. Both closing and opening prices to offer price were incorporated to measure the initial return on the initial day of trading. The outcomes showed that after the reform of book building pricing mechanism, the initial return of IPOs increased, when compared to the fixed price offerings in Pakistan. This study concludes that information from book building pricing mechanism and premium had influenced both issuer and investor in subscribing IPO.

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