scholarly journals Interconnectedness between Commodity Futures and Spot Prices: A Comparative Analysis between Ordinary Least Square (OLS) and Quantile Regression (QR)

2021 ◽  
Vol 12 (03) ◽  
pp. 151-167
Author(s):  
Cosmos Amoah
Keyword(s):  
Comparative Analysis ◽  
Quantile Regression ◽  
Least Square ◽  
Commodity Futures ◽  
Spot Prices ◽  
Ordinary Least Square

Socio demographic determinants of BMI of Pakistani women: an evidence from PDHS (2017-18) using quantile regression analysis

2021 ◽  
Author(s):  
Asifa Kamal ◽  
Aqsa Asghar Ali ◽  
Sameena Irfan
Keyword(s):  
Quantile Regression ◽  
Economic Status ◽  
Household Head ◽  
Secondary Data ◽  
Wealth Index ◽  
Least Square ◽  
Obese Women ◽  
Ordinary Least Square ◽  
Under Nutrition ◽  
Pakistani Women

Abstract Objective: To explore the socio demographic determinants of nutritional status of Pakistani women. Methods: Secondary data from recent Pakistan Demographic and Health Survey (PDHS 2017-18) is taken. Data collection period is from 22 November 2017 to 30 April 2018. Ordinary least square (OLS) and quantile regression (QR) models are used for analysis. Results: QR model is found appropriate for BMI data to capture effect at different level of distribution of BMI. Less than 5% women are under nutrition for some categories of factors. Age of women, women’s education, frequency of watching TV, wealth index, husband’s education and region (KPK, Balochistan) showed a positive effect on women’s BMI in Pakistan across all conditional distribution of BMI. In contrary, age of women at first birth, women’s agriculture or manual working status, gender of household head (female) and region (Sindh) showed negative effect on women’s BMI in Pakistan. Conclusion: It is concluded that overweight/obesity is becoming serious problem as compared to undernutrition in Pakistani women. Percentage of deprived women is little and level of under nutrition is also not alarming.  Privileged women (with respect to education, economic status, urbanization, sedentary life style) have more chances to have higher BMI (overweight or obese). Women of KPK and Balochistan are at higher risk of overweight/ obesity as compared to Punjabi women. Keywords: PDHS 2017-18, Ordinary Least Square (OLS), Quantile Regression (QR) Model, Continuous....

scholarly journals PERBANDINGAN TRANSFORMASI BOX-COX DAN REGRESI KUANTIL MEDIAN DALAM MENGATASI HETEROSKEDASTISITAS

2015 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
NI WAYAN YUNI CAHYANI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Quantile Regression ◽  
Linear Regression Analysis ◽  
Least Square ◽  
Power Transformation ◽  
Ordinary Least Square

Ordinary least square (OLS) is a method that can be used to estimate the parameter in linear regression analysis. There are some assumption which should be satisfied on OLS, one of this assumption is homoscedasticity, that is the variance of error is constant. If variance of the error is unequal that so-called heteroscedasticity. The presence heteroscedasticity can cause estimation with OLS becomes inefficient. Therefore, heteroscedasticity shall be overcome. There are some method that can used to overcome heteroscedasticity, two among those are Box-Cox power transformation and median quantile regression. This research compared Box-Cox power transformation and median quantile regression to overcome heteroscedasticity. Applied Box-Cox power transformation on OLS result ????2point are greater, smaller RMSE point and confidencen interval more narrow, therefore can be concluded that applied of Box-Cox power transformation on OLS better of median quantile regression to overcome heteroscedasticity.

Factors affecting housing prices in Izmir, Turkey: a quantile regression approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Onur Özsoy ◽  
Hasan Şahin
Keyword(s):  
Quantile Regression ◽  
House Prices ◽  
Housing Prices ◽  
Least Square ◽  
Policy Implications ◽  
Content Type ◽  
Factors Affecting ◽  
Ordinary Least Square ◽  
Major Factors ◽  
Quantile Regression Method

Purpose The purpose of this paper is to investigate empirically the main factors that affect the house prices in Izmir, Turkey using the quantile regression and ordinary least square approaches. Design/methodology/approach Sample data about the housing market for Izmir collected from the web pages of various real estate agencies during June 2018. Following this, the quantile regression method is used to estimate all possible effects of variables on each interested quantile to determine the factors that affect house prices to guide the potential consumers, house developers, city planners and the policymakers in Izmir, Turkey. Findings Results show that the age of the house, central heating and parking have no significant effect on prices. The size of the house, the existence of an elevator, fire and security have a positive and significant effect on prices. The number of rooms has lower values for high-priced houses, while the floor, the number of balconies, air conditioning, proximity to schools have a higher value for high-priced houses. The number of toilets, the number of bathrooms and the distance to the hospital have a lower value on the high-priced housing. The value of the distance from the city center and the shopping center is almost uniform in all quantiles and lowers the value of the higher-priced houses. With the exception of the value of the houses in the 10th percentile in Balcova district, the value of the houses in Konak, Balcova and Narlidere is lower prices in Karsiyaka. Originality/value This is the first comprehensive research to determine the major factors that affect house prices in Izmir. The second contribution of this paper is that it includes all possible variables and accordingly derives adequate policy implications, which could be used both by the public housing authority and private housing constructing companies in designing and implementing effective housing policies.

scholarly journals COVID-19 and tourism sector: A perspective of Saudi Arabia

2021 ◽  
Vol 8 (9) ◽  
pp. 58-62
Author(s):  
Abeer Shergawi Kernshi ◽  
◽  
Rida Waheed ◽  
Keyword(s):  
Saudi Arabia ◽  
Quantile Regression ◽  
Least Square ◽  
Current Paper ◽  
Ordinary Least Square ◽  
Tourism Sector ◽  
The Relationship

The current paper attempts to analyze the relationship between COVID-19 and tourism in Saudi Arabia from the period 2nd March 2020–15th Nov 2020 as COVID-19 cases started to increase in March 2020. Along with this, predicted data is also used for the year 2021 in order to know the situation of tourism in the coming year if COVID-19 cases continue to increase. Ordinary least square, as well as quantile regression, is used to have a detailed overview of the nexus between COVID-19 and tourism. The findings of the study clearly indicate that tourism in Saudi Arabia is declined significantly due to COVID-19 in 2020. Besides this, it is also found that if COVID-19 cases continue to increase in 2021, then tourism will continue to decrease in the coming year as well.

scholarly journals Get Acquainted with Quantile Regression

2016 ◽  
Vol 32 (1) ◽  
pp. 41-54
Author(s):  
Agung Santoso ◽  
Tri Hayuning Tyas
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Normal Distribution ◽  
Real Data ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Analysis Technique ◽  
Homogeneity Of Variance ◽  
The Mean

This article was written to introduce quantile regression (QR) analysis technique for research in Psychology. The authors present the advantages possessed by QR compared with ordinary least square (OLS) for the regression analysis approach. The QR’s main advantage than OLS is the information concerning the effects of the independent variables on the dependent variable at a location other than the mean. QR can also provide information regarding the effect of independent variables on the distribution and skewness of the dependent variable. Another QR’s advantage is associated with the robustness against violations of assumptions about the normal distribution of data and homogeneity of variance. These two advantages make the authors feel the need to introduce QR in studies in Psychology. The authors are then applying the QR on real data as an illustration. The results of the analysis in the illustration show the advantages of QR over OLS, especially in providing information on the phenomenon under study.

scholarly journals Can Cryptocurrency, Gold and Bonds Play a Role as a Safe Haven for the Indonesian Capital Market?

2021 ◽  
pp. 1
Author(s):  
Paulus Hartono ◽  
Robiyanto Robiyanto
Keyword(s):  
Quantile Regression ◽  
Capital Market ◽  
Least Square ◽  
Government Bond ◽  
Safe Haven ◽  
Bond Index ◽  
Ordinary Least Square ◽  
Indonesian Government

The purpose of this research to test the cryptocurrency, gold and bonds as safe haven assets to Indonesian capital market. The data used in this study is the daily closing data of cryptocurrency, gold (GOLDIDR) and the Indonesian Government Bond Index (IGBI) during the period on August 2015 to December 2018 obtained from Investing, Yahoo Finance, and Spindices. The analysis of the data is used Ordinary Least Square (OLS) and Quantile Regression (QREG). The results found that ethereum can be a safe haven. While bitcoin, ripple, gold, and the Indonesian Government Bond Index (IGBI) cannot be a safe haven for the Indonesian capital market.

Determinants of dividend payout of Indian manufacturing companies

2018 ◽  
Vol 10 (4) ◽  
pp. 364-376 ◽  
Author(s):  
Bhanu Pratap Singh Thakur ◽  
M. Kannadhasan
Keyword(s):  
Quantile Regression ◽  
Dividend Policy ◽  
Least Square ◽  
Firm Characteristics ◽  
Manufacturing Companies ◽  
Growth Opportunities ◽  
Content Type ◽  
Ordinary Least Square ◽  
Quantile Regression Method ◽  
Different Levels

Purpose The purpose of this study is to examine the influence of firm characteristics such as profitability, growth opportunities, size, leverage and maturity on dividend policy of Indian firms. Design/methodology/approach The study analyzes the determinants of dividend policy of manufacturing firms in India using panel data. Because of the non-linearity behaviour of dividend pay-out by firms, the study uses quantile regression method to examine whether the determinants of dividends vary depending on the company’s level of dividends. Findings Overall, the results show important difference between ordinary least square and quantile regression estimates and depict differential effect on dividend at different levels. The notable difference occurs because either the significance changes (e.g. for profitability and growth opportunities) or because the magnitude of coefficients changes (e.g. for size, profitability and growth opportunities). Originality/value This finding is useful in identifying the dividend issuing companies. Further, results of this study would be helpful to the mangers to manage their financial positions that subsequently help in retaining and attracting the probable investors.

scholarly journals Mean Estimators Using Robust Quantile Regression and L-Moments’ Characteristics for Complete and Partial Auxiliary Information

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Malik Muhammad Anas ◽  
Zhensheng Huang ◽  
David Anekeya Alilah ◽  
Ambreen Shafqat ◽  
Sajjad Hussain
Keyword(s):  
Quantile Regression ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Least Square ◽  
R Language ◽  
Ordinary Least Square ◽  
Numerical Studies ◽  
Regression Estimators ◽  
L Moments ◽  
Class Of Estimators

Ratio type regression estimator is a prevalent and readily implemented heuristic under simple random sampling (SRS) and two-stage sampling for the estimation of population. But this existing method is based on the ordinary least square (OLS) regression coefficient which is not an effective approach in the presence outliers in the data. In this article, we proposed a class of estimators firstly for complete auxiliary information and, later on, for partial auxiliary information for the presence of outliers in the data. To address this problem, initially we presented a distinct class of estimators by introducing the characteristics of L-moments in the existing estimators. Later on, quantile regression estimators are defined as more robust in the presence of outliers. These techniques empowered the proposed estimators to handle the problem of outliers. To prove the better performance of the proposed estimators, numerical studies are carried out using R language. To calculate the mean square error (MSE), hypothetical equations are expressed for adapted and proposed estimators. Percentage Relative Efficiencies (PRE) are compared to justify the proposed estimators.

Dampak Kebijakan Moneter Pada Output Di Negara-Negara ASEAN Tahun 1980-2014

2017 ◽  
Vol 4 (1) ◽  
pp. 58-70
Author(s):  
Nur Widiastuti
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Fixed Effects ◽  
Empirical Studies ◽  
Negative Relationship ◽  
Least Square ◽  
Fixed Effects Model ◽  
State Data ◽  
Ordinary Least Square ◽  
The Impact

The Impact of monetary Policy on Ouput is an ambiguous. The results of previous empirical studies indicate that the impact can be a positive or negative relationship. The purpose of this study is to investigate the impact of monetary policy on Output more detail. The variables to estimatate monetery poicy are used state and board interest rate andrate. This research is conducted by Ordinary Least Square or Instrumental Variabel, method for 5 countries ASEAN. The state data are estimated for the period of 1980 – 2014. Based on the results, it can be concluded that the impact of monetary policy on Output shown are varied.Keyword: Monetary Policy, Output, Panel Data, Fixed Effects Model

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