Impacts of Fiscal Decentralization on Regional Income Disparity: Distribution Effects Using Panel Quantile Regression Model

2017 ◽  
Vol 21 (3) ◽  
pp. 1-37
Author(s):  
Kwang-Min Moon
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Fiscal Decentralization ◽  
Income Disparity ◽  
Regional Income ◽  
Quantile Regression Model ◽  
Panel Quantile Regression

scholarly journals ON THE IMPACT OF REAL ESTATE PRICES ON THE DEVELOPMENT OF REGIONAL ECONOMY IN CHINA–AN ESTIMATION BASED ON PANEL QUANTILE REGRESSION MODEL

2021 ◽  
Vol 5 (2) ◽  
pp. 51-54
Author(s):  
Baili Zhang ◽  
Yadong Ma ◽  
Mengyue Yin ◽  
Zhengxun Li
Keyword(s):  
Economic Development ◽  
Real Estate ◽  
Quantile Regression ◽  
Regression Model ◽  
Marginal Effect ◽  
Quantile Regression Model ◽  
Panel Quantile Regression ◽  
Real Estate Prices ◽  
Impact Of Urbanization ◽  
The Impact

The paper analyzes the mechanism of real estate prices on economic development with panel quantile regression model. It is found that real estate prices can significantly promote economic development. Generally speaking, the contribution of real estate prices to economic development in regions with higher level of economic development is higher than that in regions with lower level. With the continuous improvement of the quantile, the impact of real estate prices has generally increased gradually, and the impact of urbanization level basically shows the law of diminishing marginal effect.

Non-linear impact of globalization on financial crimes: a case of developing economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabia Muhammad Amjad ◽  
Abdul Rafay ◽  
Noman Arshed ◽  
Mubbasher Munir ◽  
Maryam Muhammad Amjad
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Money Laundering ◽  
Developing Economies ◽  
Financial Crime ◽  
Content Type ◽  
Quantile Regression Model ◽  
Panel Quantile Regression ◽  
Financial Crimes ◽  
Non Linear

Purpose The Financial Action Task Force defines money laundering as “processing of these criminal proceeds to disguise their illegal origin”. This is the major portion of financial crime that has ties across borders and like all financial crimes which are well planned and camouflaged, this crime is difficult to detect and deter. Over the years, on one side, globalization has provided development opportunities, it has also become one reason for the pervasiveness of money laundering. This has led to a disturbance in the global financial system and social unrest as proceeds from money laundering are being used in terrorism. The purpose of this study is to explore the non linear effect of globalization on financial crime in the form of money laundering. Design/methodology/approach An investigation based on 119 developing countries from the time period of 1985 till 2015 is conducted in this study. The panel quantile regression model was used to estimate antecedents of money laundering. Findings The study confirmed that globalization follows an inverted U-shaped relationship with money laundering. Furthermore, indicators such as investment portfolio and socioeconomic conditions have a significant effect on money laundering. Originality/value The panel quantile regression model was used to estimate antecedents of money laundering.

scholarly journals International Tourism, Digital Infrastructure, and CO2 Emissions: Fresh Evidence From Panel Quantile Regression Approach

2021 ◽  
Author(s):  
Liu Wei ◽  
Sana ullah
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Co2 Emissions ◽  
Environmental Quality ◽  
Quantile Regression Model ◽  
Digital Infrastructure ◽  
Robust Model ◽  
Tourism Sector ◽  
Panel Quantile Regression ◽  
Regression Techniques

Abstract The main motivation behind this study is the importance of the tourism sector and digitalization in the economic development of a country and their potential effects on the country's environmental quality. For empirical analysis, the study applies FMOLS, DOLS, and quantile regression techniques for Asian economies. The findings of the study confirmed that tourism and digitalization improve environmental quality in FMOLS and DOLS models. In the basic quantile regression model, the estimates attached to tourism arrival are positive 5th quantile to 40th quantile and then turn negative from 60th quantile and onwards. Likewise, the estimates attached to tourism receipts in the robust quantile regression model are positive from quantile 5th to quantile 20 and negative and increasing from quantile 30 and onwards. Conversely, the estimates of digital infrastructure are insignificant in the basic quantile model at all quantiles except 95th. However, the estimated coefficients of digital infrastructure in the robust model are negative and rising from 40th quantile to 70th quantile and negative and declining from 80th quantile to 95th quantile. In general, we can say that as the tourism and digital sectors grow, the CO2 emissions decline.

scholarly journals The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model

ETIKONOMI ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 225-238
Author(s):  
Noreen Khalid ◽  
Raja Fawad Zafar ◽  
Qasim Raza Syed ◽  
Roni Bhowmik
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Regression Model ◽  
Stock Returns ◽  
Market Returns ◽  
Stock Market Returns ◽  
Quantile Regression Model ◽  
Heterogeneous Effects ◽  
Panel Quantile Regression ◽  
The Impact

The purpose of this study is to probe the impact of the novel coronavirus (COVID-19) outbreak on stock market returns and volatility in developed markets. We employ a panel quantile regression model to capture unobserved individual heterogeneity and distributional heterogeneity. The study's findings reveal that there is a heterogeneous impact of COVID-19 on stock market returns and volatility. More specifically, there is a negative impact of COVID-19 on stock returns in the bearish stock market; however, there is an insignificant impact of COVID-19 on stock returns in the bullish stock market. Furthermore, COVID-19 has a positive impact on stock market volatility across all quantiles.JEL Classification: G24, G30, O16How to Cite:Khalid, N., Zafar, R. F., Syed, Q. R., Bhowmik, R., & Jamil, M. (2021). The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model. Etikonomi, 20(2), xx – xx. https://doi.org/10.15408/etk.v20i2.20587.

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