scholarly journals Determinants of Poverty in Mexico: A Quantile Regression Analysis

Economies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 60
Author(s):  
Jorge Garza-Rodriguez ◽  
Gustavo A. Ayala-Diaz ◽  
Gerardo G. Coronado-Saucedo ◽  
Eugenio G. Garza-Garza ◽  
Oscar Ovando-Martinez
Keyword(s):  
Quantile Regression ◽  
Household Head ◽  
Elderly Person ◽  
Probit Model ◽  
Ordinary Least Squares ◽  
Indigenous Language ◽  
Poor People ◽  
Explanatory Variables ◽  
Female Head ◽  
Quantile Regression Analysis

Most studies on the determinants of poverty do not consider that the relative importance of each of these determinants can vary depending on the degree of poverty suffered by each group of poor people. For Mexico’s case, the studies carried out so far do not contemplate this approach, even though there is wide variation in the degree of poverty among the different groups of the poor. Investigating these differences is important to design better policies for fighting poverty, which consider how each variable that explains poverty affects each group of people who suffer from poverty differently. This article examines the determinants of poverty for Mexican households. Using data from the Mexican National Household Income and Expenditure Survey (ENIGH) 2018, the study estimates a probit model and a quantile regression model to examine the extent to which the determinants of poverty vary across the poverty spectrum. The results from the probit model indicate that households with more than one member, having a female head, or speaker of an indigenous language are more likely to be poor. The results obtained in the quantile regressions indicate that there are significant differences with the results of the simple ordinary least squares model, especially for households in extreme poverty but also for the other income categories analyzed for several of the explanatory variables used in the models. Households in the categories extremely poor and deeply poor are most affected if they are in the southern region or if the household head speaks an indigenous language or is an elderly person. It is observed that achieving a higher educational level is an effective way to increase income across the poverty spectrum.

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.

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 Quantile regression analysis of the association between parental rearing and interpersonal sensitivity in Chinese adolescents

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yafeng Zhang ◽  
Wei Tian ◽  
Yuqi Xin ◽  
Quan Zhou ◽  
Guangcan Yan ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Influencing Factor ◽  
Ordinary Least Squares ◽  
Interpersonal Sensitivity ◽  
Family Education ◽  
Cross Sectional ◽  
Definitive Evidence ◽  
Maternal Rejection ◽  
Quantile Regression Analysis ◽  
Parental Rearing

Abstract Background Parental rearing is well documented as an important influencing factor of interpersonal sensitivity (IS). However, little research has focused on the extent by which various aspects of parental rearing in fluence IS. This study aimed to analyze the effects of parental rearing on IS, using quantile regression. We analyzed the extent of the influence of parental rearing on IS by quantile regression to provide definitive evidence on the family education of adolescents with IS problems. Methods The multiple cross-sectional studies were conducted among 3345 adolescents from Harbin, China, in 1999, 2006, 2009 and 2016. Furthermore, a multistage sampling method (stratified random cluster) was used to select participants. IS was assessed using a subscale of the Symptom Checklist-90-Revision. Perceived parental rearing was assessed using the Egna Minnen av. Barndoms Uppfostran. The ordinary least squares (OLS) linear regression was used to determine the average effect of parental rearing on IS. The quantile regression was conducted to examine the established associations and to further explain the association. Results Paternal emotional warmth was found to be associated with IS across the quantile, especially after the 0.6 quantiles; however, this association was not found for maternal emotional warmth. Paternal punishment was associated with IS at the 0.22–0.27 and 0.60 quantile; however, maternal punishment had no significant effect on IS. QR method found that paternal overinvolvement was associated with IS at the 0.48–0.65 quantiles, but paternal overprotection was associated with IS across the quantile; however, maternal overinvolvement and overprotection was positively correlated with IS at the 0.07–0.95 quantiles. The correlation between paternal rejection and IS was found at the 0.40–0.75 and > 0.90 quantiles; maternal rejection was associated with IS within the 0.05–0.92 quantiles. Conclusions Parental rearing practices predict different magnitudes of IS at varying levels. This study provides suggestions for parents to assess purposefully and systematically, intervene, and ameliorate adolescent IS problems. We also highlight the role of paternal rearing in children’s IS problems, providing new ideas for family education.

scholarly journals Association between leukocyte telomere length and sex by quantile regression analysis

2021 ◽  
Author(s):  
Fernanda Gutierrez-Rodrigues ◽  
Raquel M. Alves-Paiva ◽  
Natália F. Scatena ◽  
Edson Z. Martinez ◽  
Priscila S. Scheucher ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Telomere Length ◽  
Leukocyte Telomere Length ◽  
Quantile Regression Analysis

Impact of Macroeconomic Variables on Exchange Rate Uncertain

2021 ◽  
Vol 17 (3) ◽  
pp. 47-55
Author(s):  
Jane Kaboro ◽  
Naftaly Mose
Keyword(s):  
Exchange Rate ◽  
Monetary Union ◽  
Empirical Studies ◽  
Ordinary Least Squares ◽  
Exchange Rate Uncertainty ◽  
Member States ◽  
East African Community ◽  
Macroeconomic Variables ◽  
Explanatory Variables ◽  
Negative Effect

Abstract Macroeconomic convergence is critical for member states to achieve the level of harmonization required for establishing a stable and resilient monetary union. The East African Community (EAC) member states, therefore, established set targets for macroeconomic convergence, intending to eliminate exchange rate uncertainty within the bloc and reduce the costs of the monetary union. However, recent empirical studies indicate that the rate of convergence of the member states to the set macroeconomic targets has been very slow, resulting in high exchange rate uncertainty within the region. It is against this backdrop that this research was conceptualized to examine the influence of convergence in macroeconomic variables on the exchange rate uncertainty of EAC states using secondary panel data. The study made use of standard deviation and the Levin Lin Chu (LLC) test to determine convergence and unit root respectively. The panel ordinary least squares (OLS) regression findings showed that all the explanatory variables had a negatively significant effect on exchange rate uncertainty. This implies that convergence in macroeconomic variables among the member countries slows exchange rate uncertainty. Thus, policy should be made towards controlling this negative effect resulting from macroeconomic variables as East Africa bids for monetary union.

A quantile regression approach to evaluate the relative impact of global and local factors on the MENA stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Slah Bahloul ◽  
Nawel Ben Amor
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Stock Markets ◽  
Financial Integration ◽  
Ordinary Least Squares ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Impact

PurposeThis paper investigates the relative importance of local macroeconomic and global factors in the explanation of twelve MENA (Middle East and North Africa) stock market returns across the different quantiles in order to determine their degree of international financial integration.Design/methodology/approachThe authors use both ordinary least squares and quantile regressions from January 2007 to January 2018. Quantile regression permits to know how the effects of explanatory variables vary across the different states of the market.FindingsThe results of this paper indicate that the impact of local macroeconomic and global factors differs across the quantiles and markets. Generally, there are wide ranges in degree of international integration and most of MENA stock markets appear to be weakly integrated. This reveals that the portfolio diversification within the stock markets in this region is still beneficial.Originality/valueThis paper is original for two reasons. First, it emphasizes, over a fairly long period, the impact of a large number of macroeconomic and global variables on the MENA stock market returns. Second, it examines if the relative effects of these factors on MENA stock returns vary or not across the market states and MENA countries.

Determinants of liquidity in Malaysian SMEs: a quantile regression approach

2018 ◽  
Vol 67 (9) ◽  
pp. 1566-1584 ◽  
Author(s):  
Shaista Wasiuzzaman
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Emerging Market ◽  
Developed Countries ◽  
Data Availability ◽  
Firm Level ◽  
Liquidity Management ◽  
Ols Regression ◽  
Content Type ◽  
Quantile Regression Analysis

PurposeThe management of liquidity has always been seen as a critical but often ignored issue in finance. Despite the abundance of studies on liquidity management, these studies mainly focus on developed countries and on large firms. Liquidity is critical for the small firm but studies on liquidity management in small and medium enterprises (SMEs) are lacking. The purpose of this paper is to examine the firm-level determinants of liquidity of SMEs in Malaysia.Design/methodology/approachData are collected for a total of 986 small firms in Malaysia from 2011 to 2014, resulting in a total of 2,683 observations. Firm-specific variables and the effect of the economy are considered as the possible determinants of liquidity. Ordinary least squares (OLS) regression analysis with standard errors adjusted for firm-level clustering and quantile regression analysis are used for this purpose.FindingsAnalysis using OLS regression technique indicates that a firm’s profitability, its growth, asset tangibility, size, age and firm status are significant factors in influencing its liquidity decision. Leverage and economic condition are not found to have any significant influence on liquidity. However, quantile regression analysis provides a different picture especially for SMEs with liquidity at the quantile levels ofθ=0.10 and 0.90. Atθ=0.10, only profitability, tangibility and firm status are significant, while atθ=0.90, tangibility, size, firm status and, to some extent, age are significant in influencing liquidity levels.Originality/valueTo the author’s knowledge, this is the first study analyzing the liquidity decision of SMEs in an emerging market such as Malaysia. Most studies on liquidity management of SMEs are focused on developed countries due to data availability but these studies are also only a handful. Additionally, this study uses quantile regression analysis which highlights the need to analyze financial decisions at different levels rather than at the aggregate level as done in OLS regression analysis.

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