Testing Porter and pollution haven hypothesis via economic variables and CO2 emissions: a cross-country review with panel quantile regression method

2020 ◽  
Vol 27 (25) ◽  
pp. 31527-31542 ◽  
Author(s):  
Narges Salehnia ◽  
Nooshin Karimi Alavijeh ◽  
Nasrin Salehnia
Keyword(s):  
Quantile Regression ◽  
Co2 Emissions ◽  
Regression Method ◽  
Pollution Haven Hypothesis ◽  
Economic Variables ◽  
Pollution Haven ◽  
Panel Quantile Regression ◽  
Cross Country ◽  
Quantile Regression Method

scholarly journals Dynamic Causality Among FDI, Economic Growth and CO2 Emissions in India With Open Markets and Technology Gap

2020 ◽  
Vol 11 (3) ◽  
pp. 15-31 ◽  
Author(s):  
Ritu Rana ◽  
Manoj Sharma
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Trade Openness ◽  
World Bank Group ◽  
Pollution Haven Hypothesis ◽  
Technology Gap ◽  
Development Indicators ◽  
Pollution Haven ◽  
Bank Group

This study examines the causality relationships between FDI, economic growth (in terms of GDP) and the natural environment, in terms of CO2 emissions and energy consumption, along with two more variables of interest i.e., trade openness and technology gap in the context of India. The data used in the study is obtained from World Development Indicators (WDI) of the World Bank Group for the period 1980 to 2014. The study employed the dynamic multivariate Toda-Yamamoto (TY) approach that uses the modified Wald (MWALD) test. The results show that FDI is neither causing economic growth nor is it bridging the technology gap directly in India. The results also indicate the existence of a Pollution Haven Hypothesis in India as the FDI is causing environmental degradation, i.e. CO2 emissions and energy consumption. Also, FDI is, though not causing GDP directly, but is doing so indirectly through CO2 emissions validating the existence of Pollution Haven Hypothesis. FDI is causing trade openness in India, but that openness is again causing more FDI which is doing no good for India.

scholarly journals Friction Characteristics of Post-Tensioned Tendons of Full-Scale Structures Based on Site Tests

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haoyun Yuan ◽  
Yuan Li ◽  
Bin Zhou ◽  
Shuanhai He ◽  
Peizhi Wang
Keyword(s):  
Friction Coefficient ◽  
Quantile Regression ◽  
Least Square Method ◽  
Regression Method ◽  
Full Scale ◽  
Least Square ◽  
Friction Characteristics ◽  
Bayesian Quantile Regression ◽  
Prestressing Force ◽  
Quantile Regression Method

In the design of prestressing concrete structures, the friction characteristics between strands and channels have an important influence on the distribution of prestressing force, which can be considered comprehensively by curvature and swing friction coefficients. However, the proposed friction coefficient varies widely and may lead to an inaccurate prestress estimation. In this study, four full-scale field specimens were established to measure the friction loss of prestressing tendons with electromagnetic sensors and anchor cable dynamometers to evaluate the friction coefficient. The least square method and Bayesian quantile regression method were adopted to calculate the friction coefficient, and the results were compared with that in the specifications. Field test results showed that Bayesian quantile regression method was more effective and significant in the estimation of the friction coefficient.

scholarly journals Statistically Efficient Construction of α-Risk-Minimizing Portfolio

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Hiroyuki Taniai ◽  
Takayuki Shiohama
Keyword(s):  
Monte Carlo ◽  
Quantile Regression ◽  
Asymptotic Normality ◽  
Asymptotic Properties ◽  
Regression Method ◽  
General Reference ◽  
Regression Problem ◽  
Efficient Construction ◽  
Consistency And Asymptotic Normality ◽  
Quantile Regression Method

We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004), an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008) to the problem of constructing α-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated.

scholarly journals ANALISIS PERANAN EMAS DAN OBLIGASI PEMERINTAH SEBAGAI SAFE HAVEN PERIODE 2014—2018

2020 ◽  
Vol 16 (2) ◽  
pp. 212-236
Author(s):  
Evamelia Evamelia ◽  
Yunia Panjaitan
Keyword(s):  
Quantile Regression ◽  
Capital Market ◽  
Regression Method ◽  
Government Policies ◽  
Safe Haven ◽  
Government Bonds ◽  
Bear Market ◽  
Political Conditions ◽  
Quantile Regression Method

The purpose of this research to identify the role of gold and government bonds role as safe haven in Indonesian capital market during 2014-2018. In this study we analyze the influence of stock on gold and government return on bear market conditions, using quantile regression. The quantile regression method was used to analyze the data.  The result if this study indicated that gold and government bonds cannot play a safe haven consistently throughout the study period due to political conditions, government policies and psychological factors (doubt) from investors. For the following research, researchers should examine more deeply about the factors that influence the loss of the role of safe haven in both investment instruments.

scholarly journals EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PENCILAN PONTENSIAL

2020 ◽  
Vol 14 (2) ◽  
pp. 305-312
Author(s):  
Netti Herawati
Keyword(s):  
Quantile Regression ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Robust Regression ◽  
Information Criterion ◽  
Regression Method ◽  
Least Square ◽  
Mean Square ◽  
The Impact ◽  
Quantile Regression Method

Abstrak Regresi kuantil sebagai metode regresi yang robust dapat digunakan untuk mengatasi dampak kasus yang tidak biasa pada estimasi regresi. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas regresi kuantil untuk menangani pencilan potensial dalam regresi linear berganda dibandingkan dengan metode kuadrat terkecil (MKT). Penelitian ini menggunakan data simulasi dengan p=3; n = 20, 40, 60, 100, 200 and   and  diulang 1000 kali. Efektivitas metode regresi kuantil dan MKT dalam pendugaan parameter β diukur dengan Mean square error (MSE) dan Akaike Information Criterion (AIC). Hasil penelitian menunjukkan bahwa regresi kuantil mampu menangani pencilan potensial dan memberikan penaksir yang lebih baik dibandingkan dengan MKT berdasarkan nilai MSE dan AIC. Kata kunci: AIC, MSE, pencilan, regresi kuantil Abstract Quantitative regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimation. The purpose of this study is to evaluate the effectiveness of quantile regression to deal with potential outliers in multiple linear regression compared to the least squares methodordinary least square (OLS).   This study uses simulation data with p=3; n = 20, 40, 60, 100, 200 and   and  repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β   parameters was measured by Mean square error (MSE) and Akaike Information Criterion (AIC). The results showed that quantile regression was able to handle potential outliers and provide better predictors compared to MKT based on MSE and AIC values. Keywords: AIC, MSE, outliers, quantile regression

Sign in / Sign up

Export Citation Format

Share Document